Why Social-Bookmarking help SEO?

Social bookmarking allows the users to store, organize, share and search bookmarks of web pages. Social bookmarking enables the internet users to access and add their favorite bookmarks from any computer. To organize these bookmarks personalized and informal tags are used. These sites helps to measure the popularity of the given site compare to other site. Basically, everything on this site is someone’s favorite. All you need to do is have look on it.
There are lot of uses and need of using social bookmarking like it helps to share your favorites with friends, family, coworkers and anyone else you wanted to. By creating a shared account by a group t collect and organize bookmarks that are useful to entire group. In short, social bookmarking saves lot of time. It helps to share information sitting at home on your own system. As today time is so precious.
Social bookmarking also helps SEO due to its immense popularity of being in front page. This also increases traffic to your sites. In long run, number of inbound links to your site will increase and regard you as a link reference. Social bookmarking uses this method of increasing traffic as their media platform with the viral marketing campaign.
Therefore, social bookmarking is a great way to increase traffic and potential customer to your website. But in order o be successful social bookmarking, you are required to provide the content that worth and has value in it.

Google webmaster holiday ideas

Google compiled a list of quick and simple tips for websites preparing for the holiday rush. For online and offline retailers, we understand that your website is a big part of your business, especially this time of year. Whether it’s to make the sale online or to increase foot traffic to your brick-and-mortar location, your web presence is a critical part of your business plan. The tips below are fast, free, and can make a big difference.Verify that your site is indexed by Google (and is returned in search results)Check your snippet content and page titles with the site: command [site:example.com] — do they look accurate and descriptive for users? Ideally, each title and snippet should be unique in order to reflect that each URL contains unique content. If anything is missing or you want more details, you can also use the Content Analysis tool in Webmaster Tools. There you can see which URLs on your site show duplicate titles or meta descriptions.
Label your images accurately

Don’t miss out on potential customers! Because good ‘alt’ text and descriptive filenames help us better understand images, make sure you change non-descriptive file names [001.jpg] to something more accurate [NintendoWii.jpg]. Image Search is one of our largest search properties, so you should take advantage of it.Know what Google knows (about your site)Check for crawl errors and learn the top queries that bring traffic to your site through Webmaster Tools. See our diagnostics checklist.Have a plan for expiring and temporary pagesMake sure to serve accurate HTTP status codes. If you no longer sell a product, serve a 404. If you have changed a product page to a new URL, serve a 301 to redirect the old page to the new one. Keeping your site up-to-date can help bring more targeted traffic your way.Increase foot traffic tooIf your website directs customers to a brick-and-mortar location, make sure you claim and double check your business listing in Google Local.
Usability 101Test the usability of your checkout process with various browsers. Ask yourself if a user can get from product page to checkout without assistance. Is your checkout button easy to find?

A role of SEO consulting specialist and how to find them

When you look in to finding a SEO consulting specialist, there are few recommended places you can start with. One way is simple to search them Via Google. Many, if not all consultants in this field advertise online. Just go to their site and take a look around. How well it match your own in content and feel? Working with SEO consultant specialist is just like investigating any other business opportunity. Another way to go about finding a SEO consultant specialist is to go through some of directories that will categorize them for you. These directories are places where consultants themselves have left their information and serve as of a yellow pages listing for people in the field. These directories are distribution release useful, so that the client can post review of the services they received. Make sure that you check the review area thoroughly before you commit anything to anyone. But at the same time it does not necessarily mean that bad review in professional in question is bad at his job.
Finding a good SEO consultant can position your website far ahead of the competition, in particular if you are just getting started. When people carry out searches, they are looking for immediate appropriate information where to send press release problem and if you are the first link they find to click, you will have a huge advantage over your competitors. Because of this, a SEO consulting specialist can help your business move forward quite quickly. Always be sure to look into their methods. One technique that SEO consulting specialists will often do is offer to host your website. While there are several other reasons to do so, that is putting you in a network of related but not competing services? There are also some strategies that will not help you in the long run. If SEO consulting offers you more hits, but its providing them by hiring people to go to your site, the traffic to your site increase, but your business will not world press release. On the other hand, a SEO consulting specialist who really understands the way search engines operate and can suggest solid technique, like what to add to your site to bring more relevant business to it or who can work with things like targeted pay per click advertising can turn out to be someone quite important to your business.
If you are looking into finding a SEO consulting specialist, make your choices wisely and always make sure that you get a specialist who will explain to you what he is doing and why.

Cuil denies spamming our blogs with comment spam

Cuil’s official denial of comment spam with cuil.com link

“We’ve received a few reports of spam comments on various blogs that “advertise” Cuil, among other sites. We at Cuil would like to assure everyone out there that we have nothing to do with this. We have not and would not engage in this activity. It is likely that someone is posting links to sites such as ours in an effort to disguise their true intent and trick comment filtering systems. Search engine guru Danny Sullivan of Search Engine Land has commented on one post agreeing with this line of thinking.
So, to put the record straight: it’s not cool, and it’s not us. We use the Web too, and these kinds of things annoy us as much as they annoy you.
As a side note, we encourage webmasters to use the nofollow attribute for links in their comments and forums. Google (and others) indicate they use this as a signal to ignore links. If this practice becomes more common, perhaps we can discourage these people from bothering with these posts in the first place.
Happy searching and enjoy the holiday season!”

Malware problem how to request review by Google

ReviewsYou can request a review via Google’s Webmaster Tools and you can see the status of the review there. If you think the review is taking too long, make sure to check the status. Finding all the malware on a site is difficult and the automated scanners are far more accurate than humans. The scanners may have found something you’ve missed and the review may have failed. If your site has a malware label, Google’s Webmaster Tools will also list some sample URLs that have problems. This is not a full list of all of the problem URLs (because that’s often very, very long), but it should get you started. Finally, don’t confuse a malware review with a request for reconsideration. If Google’s automated scanners find malware on your website, the site will usually not be removed from search results. There is also a different process that removes spammy websites from Google search results. If that’s happened and you disagree with Google, you should submit a reconsideration request. But if your site has a malware label, a reconsideration request won’t do any good — for malware you need to file a malware review from the Overview page.How long will a review take? Webmasters are eager to have a Google malware label removed from their site and often ask how long a review of the site will take. Both the original scanning and the review process are fully automated. The systems analyze large portions of the internet, which is big place, so the review may not happen immediately. Ideally, the label will be removed within a few hours. At its longest, the process should take a day or so.

Information retrival based on Historical data – An interesting Google patent

I just read this interesting patent by Google its very interesting:

Information retrieval based on historical data
AbstractA system identifies a document and obtains one or more types of history data associated with the document. The system may generate a score for the document based, at least in part, on the one or more types of history data.
Claims
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What is claimed is:
1. A method for scoring a document, comprising: identifying a document; obtaining one or more types of history data associated with the document; and generating a score for the document based on the one or more types of history data.
2. The method of claim 1, wherein the one or more types of history data includes information relating to an inception date; and wherein the generating a score includes: determining an inception date corresponding to the document, and scoring the document based, at least in part, on the inception date corresponding to the document.
3. The method of claim 2, wherein the document includes a plurality of documents; and wherein the scoring the document includes: determining an age of each of the documents based on the inception dates corresponding to the documents, determining an average age of the documents based on the ages of the documents, and scoring the documents based, at least in part, on a difference between the ages of the documents and the average age.
4. The method of claim 2, wherein the generating a score for the document includes scoring the document based, at least in part, on an elapsed time measured from the inception date corresponding to the document.
5. The method of claim 2, wherein the inception date corresponding to the document is based on at least one of a date when a search engine first discovers the document, a date when a search engine first discovers a link to the document, and a date when the document includes at least a predetermined number of pages.
6. The method of claim 1, wherein the one or more types of history data includes information relating to a manner in which a content of the document changes over time; and wherein the generating a score includes: determining a frequency at which the content of the document changes over time, and scoring the document based, at least in part, on the frequency at which the content of the document changes over time.
7. The method of claim 6, wherein the frequency at which the content of the document changes is based on at least one of an average time between the changes, a number of changes in a time period, and a comparison of a rate of change in a current time period with a rate of change in a previous time period.
8. The method of claim 6, wherein the generating a score further includes: determining an amount by which the content of the document changes over time, and scoring the document based, at least in part, on the frequency at which and the amount by which the content of the document changes over time.
9. The method of claim 8, wherein the amount by which the content of the document changes is based on at least one of a number of new pages associated with the document within a time period, a ratio of a number of new pages associated with the document versus a total number of pages associated with the document, and a percentage of the content of the document that has changed during a time period.
10. The method of claim 8, wherein the determining an amount by which the content of the document changes includes: weighting different portions of the content of the document differently based on a perceived importance of the portions, and determining the amount by which the content of the document changes as a function of the differently weighted portions of the content.
11. The method of claim 6, wherein the document includes a plurality of documents; and wherein the scoring the document includes: determining a date on which the content of each of the documents last changed, determining an average date of change based on the determined dates on which the contents of the documents last changed, and scoring the documents based, at least in part, on a difference between the dates on which the contents of the documents last changed and the average date of change.
12. The method of claim 1, wherein the one or more types of history data includes information relating to a manner in which a content of the document changes over time; and wherein the generating a score includes: determining an amount by which the content of the document changes over time, and scoring the document based, at least in part, on the amount by which the content of the document changes over time.
13. The method of claim 12, wherein the amount by which the content of the document changes is based on at least one of a number of new pages associated with the document within a time period, a ratio of a number of new pages associated with the document versus a total number of pages associated with the document, and a percentage of the content of the document that has changed during a time period.
14. The method of claim 12, wherein the determining an amount by which the content of the document changes includes: weighting different portions of the content of the document differently based on a perceived importance of the portions, and determining the amount by which the content of the document changes as a function of the differently weighted portions of the content.
15. The method of claim 1, wherein the one or more types of history data includes information relating to how often the document is selected when the document is included in a set of search results; and wherein the generating a score includes: determining an extent to which the document is selected over time when the document is included in a set of search results, and scoring the document based, at least in part, on the extent to which the document is selected over time when the document is included in the set of search results.
16. The method of claim 15, wherein the scoring the document includes assigning a higher score to the document when the document is selected more often than other documents in the set of search results over a time period.
17. The method of claim 1, wherein the one or more types of history data includes information relating to search terms that increasingly appear in search queries over time; and wherein the generating a score includes: determining whether the document is associated with the search terms, and scoring the document based, at least in part, on whether the document is associated with the search terms.
18. The method of claim 1, wherein the one or more types of history data includes information relating to queries that remain approximately constant over time but lead to results that change over time; and wherein the generating a score includes: determining whether the document is associated with queries that lead to results that change over time, and scoring the document based, at least in part, on whether the document is associated with queries that lead to results that change over time.
19. The method of claim 1, wherein the one or more types of history data includes information relating to staleness of documents; and wherein the generating a score includes: determining whether the document is stale, and scoring the document based, at least in part, on whether the document is stale.
20. The method of claim 19, wherein the scoring the document includes: determining whether stale documents are considered favorable for a search query when the document is determined to be stale, and scoring the document based, at least in part, on whether stale documents are considered favorable for the search query when the document is determined to be stale.
21. The method of claim 20, wherein the determining whether stale documents are considered favorable for the search query is based, at least in part, on how often stale documents were selected over recent documents over time for the search query.
22. The method of claim 1, wherein the one or more types of history data includes information relating to behavior of links over time; and wherein the generating a score includes: determining behavior of links associated with the document, and scoring the document based, at least in part, on the behavior of links associated with the document.
23. The method of claim 22, wherein the behavior of links relate to at least one of appearance and disappearance of one or more links pointing to the document.
24. The method of claim 23, wherein the appearance of one or more links relates to at least one of a date that a new link to the document appears, a rate at which the one or more links appear over time, and a number of the one or more links that appear during a time period, and the disappearance of one or more links relates to at least one of a date that an existing link to the document disappears, a rate at which the one or more links disappear over time, and a number of the one or more links that disappear during a time period.
25. The method of claim 22, wherein the determining behavior of links associated with the document includes monitoring at least one of time-varying behavior of links associated with the document, how many links associated with the document appear or disappear during a time period, and whether there is a trend toward appearance of new links associated with the document versus disappearance of existing links associated with the document.
26. The method of claim 1, wherein the one or more types of history data includes information relating to freshness of links; and wherein the generating a score includes: determining freshness of links associated with the document, assigning weights to the links based on the determined freshness, and scoring the document based, at least in part, on the weights assigned to the links associated with the document.
27. The method of claim 26, wherein the freshness of a link associated with the document is based on at least one of a date of appearance of the link, a date of a change to the link, a date of appearance of anchor text associated with the link, a date of a change to anchor text associated with the link, a date of appearance of a linking document containing the link, and a date of a change to a linking document containing the link.
28. The method of claim 26, wherein the weight assigned to a link is based on at least one of how much a document containing the link is trusted, how authoritative a document containing the link is, and a freshness of a document containing the link.
29. The method of claim 26, wherein the scoring the document includes: determining an age of each link pointing to the document, determining an age distribution associated with the links based on the ages of the links, and scoring the document based, at least in part, on the age distribution associated with the links.
30. The method of claim 1, wherein the one or more types of history data includes information relating to a manner in which anchor text changes over time; and wherein the generating a score includes: identifying a change in anchor text associated with a link to the document, and scoring the document based, at least in part, on the change in anchor text associated with a link to the document.
31. The method of claim 1, wherein the one or more types of history data includes information relating to differences in documents and anchor text associated with links to the documents; and wherein the generating a score includes: determining whether a content of the document changes such that the content differs from anchor text associated with one or more links to the document, and scoring the document based, at least in part, on whether the content of the document changes such that the content differs from the anchor text associated with one or more links to the document.
32. The method of claim 1, wherein the one or more types of history data includes information relating to freshness of anchor text; and wherein the generating a score includes: determining freshness of anchor text associated with one or more links to the document, and scoring the document based, at least in part, on the freshness of anchor text associated with one or more links to the document.
33. The method of claim 32, wherein the freshness of anchor text associated with a link to the document is based on at least one of a date of appearance of the anchor text, a date of a change to the anchor text, a date of appearance of a link associated with the anchor text, a date of a change to a link associated with the anchor text, a date of appearance of the document, and a date of a change to the document.
34. The method of claim 1, wherein the one or more types of history data includes information relating to traffic associated with documents; and wherein the generating a score includes: determining characteristics of traffic associated with the document, and scoring the document based, at least in part, on the characteristics of traffic associated with the document.
35. The method of claim 34, wherein the determining characteristics of traffic associated with the document includes analyzing a traffic pattern associated with the document to identify changes in the traffic pattern over time.
36. The method of claim 1, wherein the one or more types of history data includes information relating to user behavior associated with documents; and wherein the generating a score includes: determining user behavior associated with the document, and scoring the document based, at least in part, on the user behavior associated with the document.
37. The method of claim 36, wherein the user behavior relates to at least one of a number of times that the document is selected within a set of search results and an amount of time that one or more users spend accessing the document.
38. The method of claim 1, wherein the one or more types of history data includes domain-related information corresponding to domains associated with documents; and wherein the generating a score includes: analyzing domain-related information corresponding to a domain associated with the document over time, and scoring the document based, at least in part, on a result of the analyzing.
39. The method of claim 38, wherein the scoring the document includes: determining whether the domain associated with the document is legitimate, and scoring the document based, at least in part, on whether the domain associated with the document is legitimate.
40. The method of claim 38, wherein the domain-related information is related to at least one of an expiration date of the domain, a domain name server record associated with the domain, and a name server associated with the domain.
41. The method of claim 1, wherein the one or more types of history data includes information relating to a prior ranking history of documents; and wherein the generating a score includes: determining a prior ranking history of the document, and scoring the document based, at least in part, on the prior ranking history of the document.
42. The method of claim 41, wherein the scoring the document includes: determining a quantity or rate that the document moves in rankings over a time period, and scoring the document based, at least in part, on the quantity or rate that the document moves in the rankings.
43. The method of claim 41, wherein the prior ranking history is based on at least one of a number of queries for which the document is selected as a search result over time, a rate at which the document is selected as a search result over time, seasonality, burstiness, and changes in scores over time for a URL-query pair.
44. The method of claim 41, wherein the determining a prior ranking history of the document includes monitoring a rank of the document over time for spikes in the rank.
45. The method of claim 1, wherein the one or more types of history data includes information relating to user maintained or generated data; and wherein the generating a score includes: determining whether user maintained or generated data indicates that the document is of interest to a user, and scoring the document based, at least in part, on whether the user maintained or generated data indicates that the document is of interest to a user.
46. The method of claim 45, wherein the user maintained or generated data relates to at least one of favorites lists, bookmarks, temp files, and cache files associated with one or a plurality of users.
47. The method of claim 45, wherein the scoring the document includes: analyzing the user maintained or generated data over time to identify at least one of trends to add or remove the document, a rate at which the document is added to or removed from the user maintained or generated data, and whether the document is added to, deleted from, or accessed through the user maintained or generated data, and scoring the document based, at least in part, on a result of the analyzing.
48. The method of claim 1, wherein the one or more types of history data includes information relating to growth profiles of anchor text; and wherein the generating a score includes: determining a growth profile of anchor text associated with one or more links to the document, and scoring the document based, at least in part, on the growth profile of anchor text associated with one or more links to the document.
49. The method of claim 1, wherein the one or more types of history data includes information relating to linkage of independent peers; and wherein the generating a score includes: determining a growth in a number of independent peers that include the document, and scoring the document based, at least in part, on the number of independent peers.
50. The method of claim 1, wherein the one or more types of history data includes information relating to document topics; and wherein the generating a score includes: performing topic extraction relating to the document, monitoring a topic of the document for changes over time, and scoring the document based, at least in part, on changes to the topic of the document.
51. The method of claim 1, further comprising: obtaining a search query, where the identified document is identified as relevant to the search query; and generating a relevancy score for the document based on how relevant the document is to the search query; and wherein the generating a score for the document is based, at least in part, on the one or more types of history data and the relevancy score.
52. A system for scoring a document, comprising: means for identifying a document; means for obtaining a plurality of types of history data associated with the document; and means for generating a score for the document based, at least in part, on the plurality of types of history data.
53. A system for scoring a document, comprising: a history component configured to obtain one or more types of history data associated with a document; and a ranking component configured to: generate a score for the document based, at least in part, on the one or more types of history data.
54. A method for ranking a linked document, comprising: determining an age of linkage data associated with the linked document; and ranking the linked document based on a decaying function of the age of the linkage data.
55. The method of claim 54, wherein the linkage data includes at least one link.
56. The method of claim 54, wherein the linkage data includes anchor text.
57. The method of claim 54, wherein the linkage data includes a rank based, at least in part, on links and anchor text provided by one or more linking documents and related to the linked document.
58. The method of claim 57, further comprising: determining longevity of the linkage data; deriving an indication of content update for a linking document providing the linkage data; and adjusting the ranking of the linked document based on the longevity of the linkage data and the indication of content update for the linking document.
59. The method of claim 58, wherein the adjusting the ranking includes penalizing the ranking if the longevity indicates a short life for the linkage data and boosting the ranking if the longevity indicates a long life for the linkage data.
60. The method of claim 59, wherein the adjusting the ranking further includes penalizing the ranking if at least a portion of content from the linking document is considered stale over a period of time and boosting the ranking if the portion of content from the linking document is considered updated over the period of time.
61. The method of claim 54, further comprising: determining an indication of link churn for a linking document providing the linkage data; and based on the link churn, adjusting the ranking of the linked document.
62. The method of claim 61, wherein the indication of link churn is computed as a function of an extent to which one or more links provided by the linking document change over time.
63. The method of claim 62, wherein adjusting the ranking includes penalizing the ranking if the link churn is above a threshold. ——————————————————————————–
Description
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RELATED APPLICATION
[0001] This application claims priority under 35 U.S.C. .sctn. 119 based on U.S. Provisional Application No. 60/507,617, filed Sep. 30, 2003, the disclosure of which is incorporated herein by reference.
BACKGROUND OF THE INVENTION
[0002] 1. Field of the Invention
[0003] The present invention relates generally to information retrieval systems and, more particularly, to systems and methods for generating search results based, at least in part, on historical data associated with relevant documents.
[0004] 2. Description of Related Art
[0005] The World Wide Web (“web”) contains a vast amount of information. Search engines assist users in locating desired portions of this information by cataloging web documents. Typically, in response to a user’s request, a search engine returns links to documents relevant to the request.
[0006] Search engines may base their determination of the user’s interest on search terms (called a search query) provided by the user. The goal of a search engine is to identify links to high quality relevant results based on the search query. Typically, the search engine accomplishes this by matching the terms in the search query to a corpus of pre-stored web documents. Web documents that contain the user’s search terms are considered “hits” and are returned to the user.
[0007] Ideally, a search engine, in response to a given user’s search query, will provide the user with the most relevant results. One category of search engines identifies relevant documents based on a comparison of the search query terms to the words contained in the documents. Another category of search engines identifies relevant documents using factors other than, or in addition to, the presence of the search query terms in the documents. One such search engine uses information associated with links to or from the documents to determine the relative importance of the documents.
[0008] Both categories of search engines strive to provide high quality results for a search query. There are several factors that may affect the quality of the results generated by a search engine. For example, some web site producers use spamming techniques to artificially inflate their rank. Also, “stale” documents (i.e., those documents that have not been updated for a period of time and, thus, contain stale data) may be ranked higher than “fresher” documents (i.e., those documents that have been more recently updated and, thus, contain more recent data). In some particular contexts, the higher ranking stale documents degrade the search results.
[0009] Thus, there remains a need to improve the quality of results generated by search engines.
SUMMARY OF THE INVENTION
[0010] Systems and methods consistent with the principles of the invention may score documents based, at least in part, on history data associated with the documents. This scoring may be used to improve search results generated in connection with a search query.
[0011] According to one aspect consistent with the principles of the invention, a method for scoring a document is provided. The method may include identifying a document and obtaining one or more types of history data associated with the document. The method may further include generating a score for the document based, at least in part, on the one or more types of history data.
[0012] According to another aspect, a method for scoring documents is provided. The method may include determining an age of linkage data associated with a linked document and ranking the linked document based on a decaying function of the age of the linkage data.
BRIEF DESCRIPTION OF THE DRAWINGS
[0013] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an embodiment of the invention and, together with the description, explain the invention. In the drawings,
[0014] FIG. 1 is a diagram of an exemplary network in which systems and methods consistent with the principles of the invention may be implemented;
[0015] FIG. 2 is an exemplary diagram of a client and/or server of FIG. 1 according to an implementation consistent with the principles of the invention;
[0016] FIG. 3 is an exemplary functional block diagram of the search engine of FIG. 1 according to an implementation consistent with the principles of the invention; and
[0017] FIGS. 4 is a flowchart of exemplary processing for scoring documents according to an implementation consistent with the principles of the invention.
DETAILED DESCRIPTION
[0018] The following detailed description of the invention refers to the accompanying drawings. The same reference numbers in different drawings may identify the same or similar elements. Also, the following detailed description does not limit the invention.
[0019] Systems and methods consistent with the principles of the invention may score documents using, for example, history data associated with the documents. The systems and methods may use these scores to provide high quality search results.
[0020] A “document,” as the term is used herein, is to be broadly interpreted to include any machine-readable and machine-storable work product. A document may include an e-mail, a web site, a file, a combination of files, one or more files with embedded links to other files, a news group posting, a blog, a web advertisement, etc. In the context of the Internet, a common document is a web page. Web pages often include textual information and may include embedded information (such as meta information, images, hyperlinks, etc.) and/or embedded instructions (such as Javascript, etc.). A page may correspond to a document or a portion of a document. Therefore, the words “page” and “document” may be used interchangeably in some cases. In other cases, a page may refer to a portion of a document, such as a sub-document. It may also be possible for a page to correspond to more than a single document.
[0021] In the description to follow, documents may be described as having links to other documents and/or links from other documents. For example, when a document includes a link to another document, the link may be referred to as a “forward link.” When a document includes a link from another document, the link may be referred to as a “back link.” When the term “link” is used, it may refer to either a back link or a forward link.
EXEMPLARY NETWORK CONFIGURATION
[0022] FIG. 1 is an exemplary diagram of a network 100 in which systems and methods consistent with the principles of the invention may be implemented. Network 100 may include multiple clients 110 connected to multiple servers 120-140 via a network 150. Network 150 may include a local area network (LAN), a wide area network (WAN), a telephone network, such as the Public Switched Telephone Network (PSTN), an intranet, the Internet, a memory device, another type of network, or a combination of networks. Two clients 110 and three servers 120-140 have been illustrated as connected to network 150 for simplicity. In practice, there may be more or fewer clients and servers. Also, in some instances, a client may perform the functions of a server and a server may perform the functions of a client.
[0023] Clients 110 may include client entities. An entity may be defined as a device, such as a wireless telephone, a personal computer, a personal digital assistant (PDA), a lap top, or another type of computation or communication device, a thread or process running on one of these devices, and/or an object executable by one of these device. Servers 120-140 may include server entities that gather, process, search, and/or maintain documents in a manner consistent with the principles of the invention. Clients 110 and servers 120-140 may connect to network 150 via wired, wireless, and/or optical connections.
[0024] In an implementation consistent with the principles of the invention, server 120 may include a search engine 125 usable by clients 110. Server 120 may crawl a corpus of documents (e.g., web pages), index the documents, and store information associated with the documents in a repository of crawled documents. Servers 130 and 140 may store or maintain documents that may be crawled by server 120. While servers 120-140 are shown as separate entities, it may be possible for one or more of servers 120-140 to perform one or more of the functions of another one or more of servers 120-140. For example, it may be possible that two or more of servers 120-140 are implemented as a single server. It may also be possible for a single one of servers 120-140 to be implemented as two or more separate (and possibly distributed) devices.
EXEMPLARY CLIENT/SERVER ARCHITECTURE
[0025] FIG. 2 is an exemplary diagram of a client or server entity (hereinafter called “client/server entity”), which may correspond to one or more of clients 110 and servers 120-140, according to an implementation consistent with the principles of the invention. The client/server entity may include a bus 210, a processor 220, a main memory 230, a read only memory (ROM) 240, a storage device 250, one or more input devices 260, one or more output devices 270, and a communication interface 280. Bus 210 may include one or more conductors that permit communication among the components of the client/server entity.
[0026] Processor 220 may include one or more conventional processors or microprocessors that interpret and execute instructions. Main memory 230 may include a random access memory (RAM) or another type of dynamic storage device that stores information and instructions for execution by processor 220. ROM 240 may include a conventional ROM device or another type of static storage device that stores static information and instructions for use by processor 220. Storage device 250 may include a magnetic and/or optical recording medium and its corresponding drive.
[0027] Input device(s) 260 may include one or more conventional mechanisms that permit an operator to input information to the client/server entity, such as a keyboard, a mouse, a pen, voice recognition and/or biometric mechanisms, etc. Output device(s) 270 may include one or more conventional mechanisms that output information to the operator, including a display, a printer, a speaker, etc. Communication interface 280 may include any transceiver-like mechanism that enables the client/server entity to communicate with other devices and/or systems. For example, communication interface 280 may include mechanisms for communicating with another device or system via a network, such as network 150.
[0028] As will be described in detail below, the client/server entity, consistent with the principles of the invention, perform certain searching-related operations. The client/server entity may perform these operations in response to processor 220 executing software instructions contained in a computer-readable medium, such as memory 230. A computer-readable medium may be defined as one or more physical or logical memory devices and/or carrier waves.
[0029] The software instructions may be read into memory 230 from another computer-readable medium, such as data storage device 250, or from another device via communication interface 280. The software instructions contained in memory 230 may cause processor 220 to perform processes that will be described later. Alternatively, hardwired circuitry may be used in place of or in combination with software instructions to implement processes consistent with the principles of the invention. Thus, implementations consistent with the principles of the invention are not limited to any specific combination of hardware circuitry and software.
EXEMPLARY SEARCH ENGINE
[0030] FIG. 3 is an exemplary functional block diagram of search engine 125 according to an implementation consistent with the principles of the invention. Search engine 125 may include document locator 310, history component 320, and ranking component 330. As shown in FIG. 3, one or more of document locator 310 and history component 320 may connect to a document corpus 340. Document corpus 340 may include information associated with documents that were previously crawled, indexed, and stored, for example, in a database accessible by search engine 125. History data, as will be described in more detail below, may be associated with each of the documents in document corpus 340. The history data may be stored in document corpus 340 or elsewhere.
[0031] Document locator 310 may identify a set of documents whose contents match a user search query. Document locator 310 may initially locate documents from document corpus 340 by comparing the terms in the user’s search query to the documents in the corpus. In general, processes for indexing documents and searching the indexed collection to return a set of documents containing the searched terms are well known in the art. Accordingly, this functionality of document locator 310 will not be described further herein.
[0032] History component 320 may gather history data associated with the documents in document corpus 340. In implementations consistent with the principles of the invention, the history data may include data relating to: document inception dates; document content updates/changes; query analysis; link-based criteria; anchor text (e.g., the text in which a hyperlink is embedded, typically underlined or otherwise highlighted in a document); traffic; user behavior; domain-related information; ranking history; user maintained/generated data (e.g., bookmarks); unique words, bigrams, and phrases in anchor text; linkage of independent peers; and/or document topics. These different types of history data are described in additional detail below. In other implementations, the history data may include additional or different kinds of data.
[0033] Ranking component 330 may assign a ranking score (also called simply a “score” herein) to one or more documents in document corpus 340. Ranking component 330 may assign the ranking scores prior to, independent of, or in connection with a search query. When the documents are associated with a search query (e.g., identified as relevant to the search query), search engine 125 may sort the documents based on the ranking score and return the sorted set of documents to the client that submitted the search query. Consistent with aspects of the invention, the ranking score is a value that attempts to quantify the quality of the documents. In implementations consistent with the principles of the invention, the score is based, at least in part, on the history data from history component 320.
EXEMPLARY HISTORY DATA
[0034] Document Inception Date
[0035] According to an implementation consistent with the principles of the invention, a document’s inception date may be used to generate (or alter) a score associated with that document. The term “date” is used broadly here and may, thus, include time and date measurements. As described below, there are several techniques that can be used to determine a document’s inception date. Some of these techniques are “biased” in the sense that they can be influenced by third parties desiring to improve the score associated with a document. Other techniques are not biased. Any of these techniques, combinations of these techniques, or yet other techniques may be used to determine a document’s inception date.
[0036] According to one implementation, the inception date of a document may be determined from the date that search engine 125 first learns of or indexes the document. Search engine 125 may discover the document through crawling, submission of the document (or a representation/summary thereof) to search engine 125 from an “outside” source, a combination of crawl or submission-based indexing techniques, or in other ways. Alternatively, the inception date of a document may be determined from the date that search engine 125 first discovers a link to the document.
[0037] According to another implementation, the date that a domain with which a document is registered may be used as an indication of the inception date of the document. According to yet another implementation, the first time that a document is referenced in another document, such as a news article, newsgroup, mailing list, or a combination of one or more such documents, may be used to infer an inception date of the document. According to a further implementation, the date that a document includes at least a threshold number of pages may be used as an indication of the inception date of the document. According to another implementation, the inception date of a document may be equal to a time stamp associated with the document by the server hosting the document. Other techniques, not specifically mentioned herein, or combinations of techniques could be used to determine or infer a document’s inception date.
[0038] Search engine 125 may use the inception date of a document for scoring of the document. For example, it may be assumed that a document with a fairly recent inception date will not have a significant number of links from other documents (i.e., back links). For existing link-based scoring techniques that score based on the number of links to/from a document, this recent document may be scored lower than an older document that has a larger number of links (e.g., back links). When the inception date of the documents are considered, however, the scores of the documents may be modified (either positively or negatively) based on the documents’ inception dates.
[0039] Consider the example of a document with an inception date of yesterday that is referenced by 10 back links. This document may be scored higher by search engine 125 than a document with an inception date of 10 years ago that is referenced by 100 back links because the rate of link growth for the former is relatively higher than the latter. While a spiky rate of growth in the number of back links may be a factor used by search engine 125 to score documents, it may also signal an attempt to spam search engine 125. Accordingly, in this situation, search engine 125 may actually lower the score of a document(s) to reduce the effect of spamming.
[0040] Thus, according to an implementation consistent with the principles of the invention, search engine 125 may use the inception date of a document to determine a rate at which links to the document are created (e.g., as an average per unit time based on the number of links created since the inception date or some window in that period). This rate can then be used to score the document, for example, giving more weight to documents to which links are generated more often.
[0041] In one implementation, search engine 125 may modify the link-based score of a document as follows:
H=L/log(F+2),
[0042] where H may refer to the history-adjusted link score, L may refer to the link score given to the document, which can be derived using any known link scoring technique (e.g., the scoring technique described in U.S. Pat. No. 6,285,999) that assigns a score to a document based on links to/from the document, and F may refer to elapsed time measured from the inception date associated with the document (or a window within this period).
[0043] For some queries, older documents may be more favorable than newer ones. As a result, it may be beneficial to adjust the score of a document based on the difference (in age) from the average age of the result set. In other words, search engine 125 may determine the age of each of the documents in a result set (e.g., using their inception dates), determine the average age of the documents, and modify the scores of the documents (either positively or negatively) based on a difference between the documents’ age and the average age.
[0044] In summary, search engine 125 may generate (or alter) a score associated with a document based, at least in part, on information relating to the inception date of the document.
[0045] Content Updates/Changes
[0046] According to an implementation consistent with the principles of the invention, information relating to a manner in which a document’s content changes over time may be used to generate (or alter) a score associated with that document. For example, a document whose content is edited often may be scored differently than a document whose content remains static over time. Also, a document having a relatively large amount of its content updated over time might be scored differently than a document having a relatively small amount of its content updated over time.
[0047] In one implementation, search engine 125 may generate a content update score (U) as follows:
[0048] U=f(UF, UA),
[0049] where f may refer to a function, such as a sum or weighted sum, UF may refer to an update frequency score that represents how often a document (or page) is updated, and UA may refer to an update amount score that represents how much the document (or page) has changed over time. UF may be determined in a number of ways, including as an average time between updates, the number of updates in a given time period, etc.
[0050] UA may also be determined as a function of one or more factors, such as the number of “new” or unique pages associated with a document over a period of time. Another factor might include the ratio of the number of new or unique pages associated with a document over a period of time versus the total number of pages associated with that document. Yet another factor may include the amount that the document is updated over one or more periods of time (e.g., n % of a document’s visible content may change over a period t (e.g., last m months)), which might be an average value. A further factor might include the amount that the document (or page) has changed in one or more periods of time (e.g., within the last x days).
[0051] According to one exemplary implementation, UA may be determined as a function of differently weighted portions of document content. For instance, content deemed to be unimportant if updated/changed, such as Javascript, comments, advertisements, navigational elements, boilerplate material, or date/time tags, may be given relatively little weight or even ignored altogether when determining UA. On the other hand, content deemed to be important if updated/changed (e.g., more often, more recently, more extensively, etc.), such as the title or anchor text associated with the forward links, could be given more weight than changes to other content when determining UA.
[0052] UF and UA may be used in other ways to influence the score assigned to a document. For example, the rate of change in a current time period can be compared to the rate of change in another (e.g., previous) time period to determine whether there is an acceleration or deceleration trend. Documents for which there is an increase in the rate of change might be scored higher than those documents for which there is a steady rate of change, even if that rate of change is relatively high. The amount of change may also be a factor in this scoring. For example, documents for which there is an increase in the rate of change when that amount of change is greater than some threshold might be scored higher than those documents for which there is a steady rate of change or an amount of change is less than the threshold.
[0053] In some situations, data storage resources may be insufficient to store the documents when monitoring the documents for content changes. In this case, search engine 125 may store representations of the documents and monitor these representations for changes. For example, search engine 125 may store “signatures” of documents instead of the (entire) documents themselves to detect changes to document content. In this case, search engine 125 may store a term vector for a document (or page) and monitor it for relatively large changes. According to another implementation, search engine 125 may store and monitor a relatively small portion (e.g., a few terms) of the documents that are determined to be important or the most frequently occurring (excluding “stop words”).
[0054] According to yet another implementation, search engine 125 may store a summary or other representation of a document and monitor this information for changes. According to a further implementation, search engine 125 may generate a similarity hash (which may be used to detect near-duplication of a document) for the document and monitor it for changes. A change in a similarity hash may be considered to indicate a relatively large change in its associated document. In other implementations, yet other techniques may be used to monitor documents for changes. In situations where adequate data storage resources exist, the full documents may be stored and used to determine changes rather than some representation of the documents.
[0055] For some queries, documents with content that has not recently changed may be more favorable than documents with content that has recently changed. As a result, it may be beneficial to adjust the score of a document based on the difference from the average date-of-change of the result set. In other words, search engine 125 may determine a date when the content of each of the documents in a result set last changed, determine the average date of change for the documents, and modify the scores of the documents (either positively or negatively) based on a difference between the documents’ date-of-change and the average date-of-change.
[0056] In summary, search engine 125 may generate (or alter) a score associated with a document based, at least in part, on information relating to a manner in which the document’s content changes over time. For very large documents that include content belonging to multiple individuals or organizations, the score may correspond to each of the sub-documents (i.e., that content belonging to or updated by a single individual or organization).
[0057] Query Analysis
[0058] According to an implementation consistent with the principles of the invention, one or more query-based factors may be used to generate (or alter) a score associated with a document. For example, one query-based factor may relate to the extent to which a document is selected over time when the document is included in a set of search results. In this case, search engine 125 might score documents selected relatively more often/increasingly by users higher than other documents.
[0059] Another query-based factor may relate to the occurrence of certain search terms appearing in queries over time. A particular set of search terms may increasingly appear in queries over a period of time. For example, terms relating to a “hot” topic that is gaining/has gained popularity or a breaking news event would conceivably appear frequently over a period of time. In this case, search engine 125 may score documents associated with these search terms (or queries) higher than documents not associated with these terms.
[0060] A further query-based factor may relate to a change over time in the number of search results generated by similar queries. A significant increase in the number of search results generated by similar queries, for example, might indicate a hot topic or breaking news and cause search engine 125 to increase the scores of documents related to such queries.
[0061] Another query-based factor may relate to queries that remain relatively constant over time but lead to results that change over time. For example, a query relating to “world series champion” leads to search results that change over time (e.g., documents relating to a particular team dominate search results in a given year or time of year). This change can be monitored and used to score documents accordingly.
[0062] Yet another query-based factor might relate to the “staleness” of documents returned as search results. The staleness of a document may be based on factors, such as document creation date, anchor growth, traffic, content change, forward/back link growth, etc. For some queries, recent documents are very important (e.g., if searching for Frequently Asked Questions (FAQ) files, the most recent version would be highly desirable). Search engine 125 may learn which queries recent changes are most important for by analyzing which documents in search results are selected by users. More specifically, search engine 125 may consider how often users favor a more recent document that is ranked lower than an older document in the search results. Additionally, if over time a particular document is included in mostly topical queries (e.g., “World Series Champions”) versus more specific queries (e.g., “New York Yankees”), then this query-based factor–by itself or with others mentioned herein–may be used to lower a score for a document that appears to be stale.
[0063] In some situations, a stale document may be considered more favorable than more recent documents. As a result, search engine 125 may consider the extent to which a document is selected over time when generating a score for the document. For example, if for a given query, users over time tend to select a lower ranked, relatively stale, document over a higher ranked, relatively recent document, this may be used by search engine 125 as an indication to adjust a score of the stale document.
[0064] Yet another query-based factor may relate to the extent to which a document appears in results for different queries. In other words, the entropy of queries for one or more documents may be monitored and used as a basis for scoring. For example, if a particular document appears as a hit for a discordant set of queries, this may (though not necessarily) be considered a signal that the document is spam, in which case search engine 125 may score the document relatively lower.
[0065] In summary, search engine 125 may generate (or alter) a score associated with a document based, at least in part, on one or more query-based factors.
[0066] Link-Based Criteria
[0067] According to an implementation consistent with the principles of the invention, one or more link-based factors may be used to generate (or alter) a score associated with a document. In one implementation, the link-based factors may relate to the dates that new links appear to a document and that existing links disappear. The appearance date of a link may be the first date that search engine 125 finds the link or the date of the document that contains the link (e.g., the date that the document was found with the link or the date that it was last updated). The disappearance date of a link may be the first date that the document containing the link either dropped the link or disappeared itself.
[0068] These dates may be determined by search engine 125 during a crawl or index update operation. Using this date as a reference, search engine 125 may then monitor the time-varying behavior of links to the document, such as when links appear or disappear, the rate at which links appear or disappear over time, how many links appear or disappear during a given time period, whether there is trend toward appearance of new links versus disappearance of existing links to the document, etc.
[0069] Using the time-varying behavior of links to (and/or from) a document, search engine 125 may score the document accordingly. For example, a downward trend in the number or rate of new links (e.g., based on a comparison of the number or rate of new links in a recent time period versus an older time period) over time could signal to search engine 125 that a document is stale, in which case search engine 125 may decrease the document’s score. Conversely, an upward trend may signal a “fresh” document (e.g., a document whose content is fresh–recently created or updated) that might be considered more relevant, depending on the particular situation and implementation.
[0070] By analyzing the change in the number or rate of increase/decrease of back links to a document (or page) over time, search engine 125 may derive a valuable signal of how fresh the document is. For example, if such analysis is reflected by a curve that is dropping off, this may signal that the document may be stale (e.g., no longer updated, diminished in importance, superceded by another document, etc.).
[0071] According to one implementation, the analysis may depend on the number of new links to a document. For example, search engine 125 may monitor the number of new links to a document in the last n days compared to the number of new links since the document was first found. Alternatively, search engine 125 may determine the oldest age of the most recent y % of links compared to the age of the first link found.
[0072] For the purpose of illustration, consider y=10 and two documents (web sites in this example) that were both first found 100 days ago. For the first site, 10% of the links were found less than 10 days ago, while for the second site 0% of the links were found less than 10 days ago (in other words, they were all found earlier). In this case, the metric results in 0.1 for site A and 0 for site B. The metric may be scaled appropriately. In another exemplary implementation, the metric may be modified by performing a relatively more detailed analysis of the distribution of link dates. For example, models may be built that predict if a particular distribution signifies a particular type of site (e.g., a site that is no longer updated, increasing or decreasing in popularity, superceded, etc.).
[0073] According to another implementation, the analysis may depend on weights assigned to the links. In this case, each link may be weighted by a function that increases with the freshness of the link. The freshness of a link may be determined by the date of appearance/change of the link, the date of appearance/change of anchor text associated with the link, date of appearance/change of the document containing the link. The date of appearance/change of the document containing a link may be a better indicator of the freshness of the link based on the theory that a good link may go unchanged when a document gets updated if it is still relevant and good. In order to not update every link’s freshness from a minor edit of a tiny unrelated part of a document, each updated document may be tested for significant changes (e.g., changes to a large portion of the document or changes to many different portions of the document) and a link’s freshness may be updated (or not updated) accordingly.
[0074] Links may be weighted in other ways. For example, links may be weighted based on how much the documents containing the links are trusted (e.g., government documents can be given high trust). Links may also, or alternatively, be weighted based on how authoritative the documents containing the links are (e.g., authoritative documents may be determined in a manner similar to that described in U.S. Pat. No. 6,285,999). Links may also, or alternatively, be weighted based on the freshness of the documents containing the links using some other features to establish freshness (e.g., a document that is updated frequently (e.g., the Yahoo home page) suddenly drops a link to a document).
[0075] Search engine 125 may raise or lower the score of a document to which there are links as a function of the sum of the weights of the links pointing to it. This technique may be employed recursively. For example, assume that a document S is 2 years olds. Document S may be considered fresh if n % of the links to S are fresh or if the documents containing forward links to S are considered fresh. The latter can be checked by using the creation date of the document and applying this technique recursively.
[0076] According to yet another technique, the analysis may depend on an age distribution associated with the links pointing to a document. In other words, the dates that the links to a document were created may be determined and input to a function that determines the age distribution. It may be assumed that the age distribution of a stale document will be very different from the age distribution of a fresh document. Search engine 125 may then score documents based, at least in part, on the age distributions associated with the documents.
[0077] The dates that links appear can also be used to detect “spam,” where owners of documents or their colleagues create links to their own document for the purpose of boosting the score assigned by a search engine. A typical, “legitimate” document attracts back links slowly. A large spike in the quantity of back links may signal a topical phenomenon (e.g., the CDC web site may develop many links quickly after an outbreak, such as SARS), or signal attempts to spam a search engine (to obtain a higher ranking and, thus, better placement in search results) by exchanging links, purchasing links, or gaining links from documents without editorial discretion on making links. Examples of documents that give links without editorial discretion include guest books, referrer logs, and “free for all” pages that let anyone add a link to a document.
[0078] According to a further implementation, the analysis may depend on the date that links disappear. The disappearance of many links can mean that the document to which these links point is stale (e.g., no longer being updated or has been superseded by another document). For example, search engine 125 may monitor the date at which one or more links to a document disappear, the number of links that disappear in a given window of time, or some other time-varying decrease in the number of links (or links/updates to the documents containing such links) to a document to identify documents that may be considered stale. Once a document has been determined to be stale, the links contained in that document may be discounted or ignored by search engine 125 when determining scores for documents pointed to by the links.
[0079] According to another implementation, the analysis may depend, not only on the age of the links to a document, but also on the dynamic-ness of the links. As such, search engine 125 may weight documents that have a different featured link each day, despite having a very fresh link, differently (e.g., lower) than documents that are consistently updated and consistently link to a given target document. In one exemplary implementation, search engine 125 may generate a score for a document based on the scores of the documents with links to the document for all versions of the documents within a window of time. Another version of this may factor a discount/decay into the integration based on the major update times of the document.
[0080] In summary, search engine 125 may generate (or alter) a score associated with a document based, at least in part, on one or more link-based factors.
[0081] Anchor Text
[0082] According to an implementation consistent with the principles of the invention, information relating to a manner in which anchor text changes over time may be used to generate (or alter) a score associated with a document. For example, changes over time in anchor text associated with links to a document may be used as an indication that there has been an update or even a change of focus in the document.
[0083] Alternatively, if the content of a document changes such that it differs significantly from the anchor text associated with its back links, then the domain associated with the document may have changed significantly (completely) from a previous incarnation. This may occur when a domain expires and a different party purchases the domain. Because anchor text is often considered to be part of the document to which its associated link points, the domain may show up in search results for queries that are no longer on topic. This is an undesirable result.
[0084] One way to address this problem is to estimate the date that a domain changed its focus. This may be done by determining a date when the text of a document changes significantly or when the text of the anchor text changes significantly. All links and/or anchor text prior to that date may then be ignored or discounted.
[0085] The freshness of anchor text may also be used as a factor in scoring documents. The freshness of an anchor text may be determined, for example, by the date of appearance/change of the anchor text, the date of appearance/change of the link associated with the anchor text, and/or the date of appearance/change of the document to which the associated link points. The date of appearance/change of the document pointed to by the link may be a good indicator of the freshness of the anchor text based on the theory that good anchor text may go unchanged when a document gets updated if it is still relevant and good. In order to not update an anchor text’s freshness from a minor edit of a tiny unrelated part of a document, each updated document may be tested for significant changes (e.g., changes to a large portion of the document or changes to many different portions of the document) and an anchor text’s freshness may be updated (or not updated) accordingly.
[0086] In summary, search engine 125 may generate (or alter) a score associated with a document based, at least in part, on information relating to a manner in which anchor text changes over time.
[0087] Traffic
[0088] According to an implementation consistent with the principles of the invention, information relating to traffic associated with a document over time may be used to generate (or alter) a score associated with the document. For example, search engine 125 may monitor the time-varying characteristics of traffic to, or other “use” of, a document by one or more users. A large reduction in traffic may indicate that a document may be stale (e.g., no longer be updated or may be superseded by another document).
[0089] In one implementation, search engine 125 may compare the average traffic for a document over the last j days (e.g., where j=30) to the average traffic during the month where the document received the most traffic, optionally adjusted for seasonal changes, or during the last k days (e.g., where k=365). Optionally, search engine 125 may identify repeating traffic patterns or perhaps a change in traffic patterns over time. It may be discovered that there are periods when a document is more or less popular (i.e., has more or less traffic), such as during the summer months, on weekends, or during some other seasonal time period. By identifying repeating traffic patterns or changes in traffic patterns, search engine 125 may appropriately adjust its scoring of the document during and outside of these periods.
[0090] Additionally, or alternatively, search engine 125 may monitor time-varying characteristics relating to “advertising traffic” for a particular document. For example, search engine 125 may monitor one or a combination of the following factors: (1) the extent to and rate at which advertisements are presented or updated by a given document over time; (2) the quality of the advertisers (e.g., a document whose advertisements refer/link to documents known to search engine 125 over time to have relatively high traffic and trust, such as amazon.com, may be given relatively more weight than those documents whose advertisements refer to low traffic/untrustworthy documents, such as a pornographic site); and (3) the extent to which the advertisements generate user traffic to the documents to which they relate (e.g., their click-through rate). Search engine 125 may use these time-varying characteristics relating to advertising traffic to score the document.
[0091] In summary, search engine 125 may generate (or alter) a score associated with a document based, at least in part, on information relating to traffic associated with the document over time.
[0092] User Behavior
[0093] According to an implementation consistent with the principles of the invention, information corresponding to individual or aggregate user behavior relating to a document over time may be used to generate (or alter) a score associated with the document. For example, search engine 125 may monitor the number of times that a document is selected from a set of search results and/or the amount of time one or more users spend accessing the document. Search engine 125 may then score the document based, at least in part, on this information.
[0094] If a document is returned for a certain query and over time, or within a given time window, users spend either more or less time on average on the document given the same or similar query, then this may be used as an indication that the document is fresh or stale, respectively. For example, assume that the query “Riverview swimming schedule” returns a document with the title “Riverview Swimming Schedule.” Assume further that users used to spend 30 seconds accessing it, but now every user that selects the document only spends a few seconds accessing it. Search engine 125 may use this information to determine that the document is stale (i.e., contains an outdated swimming schedule) and score the document accordingly.
[0095] In summary, search engine 125 may generate (or alter) a score associated with a document based, at least in part, on information corresponding to individual or aggregate user behavior relating to the document over time.
[0096] Domain-Related Information
[0097] According to an implementation consistent with the principles of the invention, information relating to a domain associated with a document may be used to generate (or alter) a score associated with the document. For example, search engine 125 may monitor information relating to how a document is hosted within a computer network (e.g., the Internet, an intranet or other network or database of documents) and use this information to score the document.
[0098] Individuals who attempt to deceive (spam) search engines often use throwaway or “doorway” domains and attempt to obtain as much traffic as possible before being caught. Information regarding the legitimacy of the domains may be used by search engine 125 when scoring the documents associated with these domains.
[0099] Certain signals may be used to distinguish between illegitimate and legitimate domains. For example, domains can be renewed up to a period of 10 years. Valuable (legitimate) domains are often paid for several years in advance, while doorway (illegitimate) domains rarely are used for more than a year. Therefore, the date when a domain expires in the future can be used as a factor in predicting the legitimacy of a domain and, thus, the documents associated therewith.
[0100] Also, or alternatively, the domain name server (DNS) record for a domain may be monitored to predict whether a domain is legitimate. The DNS record contains details of who registered the domain, administrative and technical addresses, and the addresses of name servers (i.e., servers that resolve the domain name into an IP address). By analyzing this data over time for a domain, illegitimate domains may be identified. For instance, search engine 125 may monitor whether physically correct address information exists over a period of time, whether contact information for the domain changes relatively often, whether there is a relatively high number of changes between different name servers and hosting companies, etc. In one implementation, a list of known-bad contact information, name servers, and/or IP addresses may be identified, stored, and used in predicting the legitimacy of a domain and, thus, the documents associated therewith.
[0101] Also, or alternatively, the age, or other information, regarding a name server associated with a domain may be used to predict the legitimacy of the domain. A “good” name server may have a mix of different domains from different registrars and have a history of hosting those domains, while a “bad” name server might host mainly pornography or doorway domains, domains with commercial words (a common indicator of spam), or primarily bulk domains from a single registrar, or might be brand new. The newness of a name server might not automatically be a negative factor in determining the legitimacy of the associated domain, but in combination with other factors, such as ones described herein, it could be.
[0102] In summary, search engine 125 may generate (or alter) a score associated with a document based, at least in part, on information relating to a legitimacy of a domain associated with the document.
[0103] Ranking History
[0104] According to an implementation consistent with the principles of the invention, information relating to prior rankings of a document may be used to generate (or alter) a score associated with the document. For example, search engine 125 may monitor the time-varying ranking of a document in response to search queries provided to search engine 125. Search engine 125 may determine that a document that jumps in rankings across many queries might be a topical document or it could signal an attempt to spam search engine 125.
[0105] Thus, the quantity or rate that a document moves in rankings over a period of time might be used to influence future scores assigned to that document. In one implementation, for each set of search results, a document may be weighted according to its position in the top N search results. For N=30, one example function might be [((N+1)-SLOT)/N].sup.4. In this case, a top result may receive a score of 1.0, down to a score near 0 for the Nth result.
[0106] A query set (e.g., of commercial queries) can be repeated, and documents that gained more than M % in the rankings may be flagged or the percentage growth in ranking may be used as a signal in determining scores for the documents. For example, search engine 125 may determine that a query is likely commercial if the average (median) score of the top results is relatively high and there is a significant amount of change in the top results from month to month. Search engine 125 may also monitor churn as an indication of a commercial query. For commercial queries, the likelihood of spam is higher, so search engine 125 may treat documents associated therewith accordingly.
[0107] In addition to history of positions (or rankings) of documents for a given query, search engine 125 may monitor (on a page, host, document, and/or domain basis) one or more other factors, such as the number of queries for which, and the rate at which (increasing/decreasing), a document is selected as a search result over time; seasonality, burstiness, and other patterns over time that a document is selected as a search result; and/or changes in scores over time for a URL-query pair.
[0108] In addition, or alternatively, search engine 125 may monitor a number of document (e.g., URL) independent query-based criteria over time. For example, search engine 125 may monitor the average score among a top set of results generated in response to a given query or set of queries and adjust the score of that set of results and/or other results generated in response to the given query or set of queries. Moreover, search engine 125 may monitor the number of results generated for a particular query or set of queries over time. If search engine 125 determines that the number of results increases or that there is a change in the rate of increase (e.g., such an increase may be an indication of a “hot topic” or other phenomenon), search engine 125 may score those results higher in the future.
[0109] In addition, or alternatively, search engine 125 may monitor the ranks of documents over time to detect sudden spikes in the ranks of the documents. A spike may indicate either a topical phenomenon (e.g., a hot topic) or an attempt to spam search engine 125 by, for example, trading or purchasing links. Search engine 125 may take measures to prevent spam attempts by, for example, employing hysteresis to allow a rank to grow at a certain rate. In another implementation, the rank for a given document may be allowed a certain maximum threshold of growth over a predefined window of time. As a further measure to differentiate a document related to a topical phenomenon from a spam document, search engine 125 may consider mentions of the document in news articles, discussion groups, etc. on the theory that spam documents will not be mentioned, for example, in the news. Any or a combination of these techniques may be used to curtail spamming attempts.
[0110] It may be possible for search engine 125 to make exceptions for documents that are determined to be authoritative in some respect, such as government documents, web directories (e.g., Yahoo), and documents that have shown a relatively steady and high rank over time. For example, if an unusual spike in the number or rate of increase of links to an authoritative document occurs, then search engine 125 may consider such a document not to be spam and, thus, allow a relatively high or even no threshold for (growth of) its rank (over time).
[0111] In addition, or alternatively, search engine 125 may consider significant drops in ranks of documents as an indication that these documents are “out of favor” or outdated. For example, if the rank of a document over time drops significantly, then search engine 125 may consider the document as outdated and score the document accordingly.
[0112] In summary, search engine 125 may generate (or alter) a score associated with a document based, at least in part, on information relating to prior rankings of the document.
[0113] User Maintained/Generated Data
[0114] According to an implementation consistent with the principles of the invention, user maintained or generated data may be used to generate (or alter) a score associated with a document. For example, search engine 125 may monitor data maintained or generated by a user, such as “bookmarks,” “favorites,” or other types of data that may provide some indication of documents favored by, or of interest to, the user. Search engine 125 may obtain this data either directly (e.g., via a browser assistant) or indirectly (e.g., via a browser). Search engine 125 may then analyze over time a number of bookmarks/favorites to which a document is associated to determine the importance of the document.
[0115] Search engine 125 may also analyze upward and downward trends to add or remove the document (or more specifically, a path to the document) from the bookmarks/favorites lists, the rate at which the document is added to or removed from the bookmarks/favorites lists, and/or whether the document is added to, deleted from, or accessed through the bookmarks/favorites lists. If a number of users are adding a particular document to their bookmarks/favorites lists or often accessing the document through such lists over time, this may be considered an indication that the document is relatively important. On the other hand, if a number of users are decreasingly accessing a document indicated in their bookmarks/favorites list or are increasingly deleting/replacing the path to such document from their lists, this may be taken as an indication that the document is outdated, unpopular, etc. Search engine 125 may then score the documents accordingly.
[0116] In an alternative implementation, other types of user data that may indicate an increase or decrease in user interest in a particular document over time may be used by search engine 125 to score the document. For example, the “temp” or cache files associated with users could be monitored by search engine 125 to identify whether there is an increase or decrease in a document being added over time. Similarly, cookies associated with a particular document might be monitored by search engine 125 to determine whether there is an upward or downward trend in interest in the document.
[0117] In summary, search engine 125 may generate (or alter) a score associated with a document based, at least in part, on user maintained or generated data.
[0118] Unique Words, Bigrams, Phrases in Anchor Text
[0119] According to an implementation consistent with the principles of the invention, information regarding unique words, bigrams, and phrases in anchor text may be used to generate (or alter) a score associated with a document. For example, search engine 125 may monitor web (or link) graphs and their behavior over time and use this information for scoring, spam detection, or other purposes. Naturally developed web graphs typically involve independent decisions. Synthetically generated web graphs, which are usually indicative of an intent to spam, are based on coordinated decisions, causing the profile of growth in anchor words/bigrams/phrases to likely be relatively spiky.
[0120] One reason for such spikiness may be the addition of a large number of identical anchors from many documents. Another possibility may be the addition of deliberately different anchors from a lot of documents. Search engine 125 may monitor the anchors and factor them into scoring a document to which their associated links point. For example, search engine 125 may cap the impact of suspect anchors on the score of the associated document. Alternatively, search engine 125 may use a continuous scale for the likelihood of synthetic generation and derive a multiplicative factor to scale the score for the document.
[0121] In summary, search engine 125 may generate (or alter) a score associated with a document based, at least in part, on information regarding unique words, bigrams, and phrases in anchor text associated with one or more links pointing to the document.
[0122] Linkage of Independent Peers
[0123] According to an implementation consistent with the principles of the invention, information regarding linkage of independent peers (e.g., unrelated documents) may be used to generate (or alter) a score associated with a document.
[0124] A sudden growth in the number of apparently independent peers, incoming and/or outgoing, with a large number of links to individual documents may indicate a potentially synthetic web graph, which is an indicator of an attempt to spam. This indication may be strengthened if the growth corresponds to anchor text that is unusually coherent or discordant. This information can be used to demote the impact of such links, when used with a link-based scoring technique, either as a binary decision item (e.g., demote the score by a fixed amount) or a multiplicative factor.
[0125] In summary, search engine 125 may generate (or alter) a score associated with a document based, at least in part, on information regarding linkage of independent peers.
[0126] Document Topics
[0127] According to an implementation consistent with the principles of the invention, information regarding document topics may be used to generate (or alter) a score associated with a document. For example, search engine 125 may perform topic extraction (e.g., through categorization, URL analysis, content analysis, clustering, summarization, a set of unique low frequency words, or some other type of topic extraction). Search engine 125 may then monitor the topic(s) of a document over time and use this information for scoring purposes.
[0128] A significant change over time in the set of topics associated with a document may indicate that the document has changed owners and previous document indicators, such as score, anchor text, etc., are no longer reliable. Similarly, a spike in the number of topics could indicate spam. For example, if a particular document is associated with a set of one or more topics over what may be considered a “stable” period of time and then a (sudden) spike occurs in the number of topics associated with the document, this may be an indication that the document has been taken over as a “doorway” document. Another indication may include the disappearance of the original topics associated with the document. If one or more of these situations are detected, then search engine 125 may reduce the relative score of such documents and/or the links, anchor text, or other data associated the document.
[0129] In summary, search engine 125 may generate (or alter) a score associated with a document based, at least in part, on changes in one or more topics associated with the document.
EXEMPLARY PROCESSING
[0130] FIG. 4 is a flowchart of exemplary processing for scoring documents according to an implementation consistent with the principles of the invention. Processing may begin with server 120 identifying documents (act 410). The documents may include, for example, one or more documents associated with a search query, such as documents identified as relevant to the search query. Alternatively, the documents may include one or more documents in a corpus or repository of documents that are independent of any search query (e.g., documents that are identified by crawling a network and stored in a repository).
[0131] Search engine 125 may obtain history data associated with the identified documents (act 420). As described above, the history data may take different forms. For example, the history data may include data relating to document inception dates; document content updates/changes; query analysis; link-based criteria; anchor text; traffic; user behavior; domain-related information; ranking history; user maintained/generated data (e.g., bookmarks and/or favorites); unique words, bigrams, and phrases in anchor text; linkage of independent peers; and/or document topics. Search engine 125 may obtain one, or a combination, of these kinds of history data.
[0132] Search engine 125 may then score the identified documents based, at least in part, on the history data (act 430). When the identified documents are associated with a search query, search engine 125 may also generate relevancy scores for the documents based, for example, on how relevant they are to the search query. Search engine 125 may then combine the history scores with the relevancy scores to obtain overall scores for the documents. Instead of combining the scores, search engine 125 may alter the relevancy scores for the documents based on the history data, thereby raising or lowering the scores or, in some cases, leaving the scores the same. Alternatively, search engine 125 may score the documents based on the history data without generating relevancy scores. In any event, search engine 125 may score the documents using one, or a combination, of the types of history data.
[0133] When the identified documents are associated with a search query, search engine 125 may also form search results from the scored documents. For example, search engine 125 may sort the documents based on their scores. Search engine 125 may then form references to the documents, where a reference might include a title of the document (which may contain a hypertext link that will direct the user, when selected, to the actual document) and a snippet (i.e., a text excerpt) from the document. In other implementations, the references are formed differently. Search engine 125 may present references corresponding to a number of the top-scoring documents (e.g., a predetermined number of the documents, documents with scores above a threshold, all documents, etc.) to a user who submitted the search query.
CONCLUSION
[0134] Systems and methods consistent with the principles of the invention may use history data to score documents and form high quality search results.
[0135] The foregoing description of preferred embodiments of the present invention provides illustration and description, but is not intended to be exhaustive or to limit the invention to the precise form disclosed. Modifications and variations are possible in light of the above teachings or may be acquired from practice of the invention. For example, while a series of acts has been described with regard to FIG. 4, the order of the acts may be modified in other implementations consistent with the principles of the invention. Also, non-dependent acts may be performed in parallel.
[0136] Further, it has generally been described that server 120 performs most, if not all, of the acts described with regard to the processing of FIG. 4. In another implementation consistent with the principles of the invention, one or more, or all, of the acts may be performed by another entity, such as another server 130 and/or 140 or client 110.
[0137] It will also be apparent to one of ordinary skill in the art that aspects of the invention, as described above, may be implemented in many different forms of software, firmware, and hardware in the implementations illustrated in the figures. The actual software code or specialized control hardware used to implement aspects consistent with the principles of the invention is not limiting of the present invention. Thus, the operation and behavior of the aspects were described without reference to the specific software code–it being understood that one of ordinary skill in the art would be able to design software and control hardware to implement the aspects based on the description herein.
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trust and authority two different things in search engines.

A webmaster world thread analyzes the difference between trust and authority in Google. Its a good thread to discuss please join here www.webmasterworld.com/google/3753332.htm

“While studying Google’s recently granted [url=http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&u=%2Fnetahtml%2FPTO%2Fsearch-adv.htm&r=1&p=1&f=G&l=50&d=PTXT&S1=7,346,839.PN.&OS=pn/7,346,839&RS=PN/7,346,839]Historical Data patent[/url], I noticed that the language helps to separate two concepts that we tend to use casually at times: trust and authority.
…links may be weighted based on how much the documents containing the links are trusted (e.g., government documents can be given high trust). Links may also, or alternatively, be weighted based on how authoritative the documents containing the links are (e.g., authoritative documents may be determined in a manner similar to that described in [url=http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&u=%2Fnetahtml%2FPTO%2Fsearch-adv.htm&r=1&p=1&f=G&l=50&d=PTXT&S1=6,285,999.PN.&OS=pn/6,285,999&RS=PN/6,285,999]U.S. Pat. No. 6,285,999)[/url].
Clearly, Google has two different metrics going on. As you can see from the reference to Larry Page’s original patent, authority in Google’s terminology comes from backlinks. When lots of other websites link to your website, you become more and more of an authority.
But that isn’t to say you’ve got trust. So what exactly is trust? Here’s an interesting section from the same patent:
…search engine 125 may monitor one or a combination of the following factors: (1) the extent to and rate at which advertisements are presented or updated by a given document over time; (2) the quality of the advertisers (e.g., a document whose advertisements refer/link to documents known to search engine 125 over time to have relatively high traffic and trust, such as amazon.com, may be given relatively more weight than those documents whose advertisements refer to low traffic/untrustworthy documents, such as a pornographic site);
So we’ve got two references here, government documents and high traffic! From other reading, I’m pretty sure that trust calculations work like this – at least in part. Google starts with a hand picked “seed list” of trusted domains. Then trust calculations can be made that flow from those domains through their links.
If a website has a direct link from a trust-seed document, that’s the next best situation to being chosen as a seed document. Lots of trust flows from that link.
If a document is two clicks away from a seed document, that’s pretty good and a decent amount of trust flows through – and so on. This is the essence of “trustrank” – a concept described in [url=http://dbpubs.stanford.edu:8090/pub/2004-17]this paper by Stanford University and three Yahoo researchers[/url].
This approach to calculating trust has been refined by the original authors to include “negative seeds” – that is, sites that are known to exist for spamming purposes. The measurements are intended to identify artifically inflated PageRank scores. See this pdf document from Stanford: [url=http://dbpubs.stanford.edu:8090/pub/showDoc.Fulltext?lang=en&doc=2005-33&format=pdf&compression=&name=2005-33.pdf]Link Spam Detection[/url]
To what degree Google follows this exact approach for calculating trust is unknown, but it’s a good bet that they share the same basic ideas. “

Top 10 Global brands – rated 2008 rankings for top brands globally.

1. Coca-Cola – The Coca-Cola Company (NYSE: KO) is the world’s largest beverage company, largest manufacturer, distributor and marketer of non-alcoholic beverage concentrates and syrups in the world, and one of the largest corporations in the United States. The company is best known for its flagship product Coca-Cola, invented by pharmacist John Stith Pemberton in 1886. The Coca-Cola formula and brand was bought in 1889 by Asa Candler who incorporated The Coca-Cola Company in 1892. Besides its namesake Coca-Cola beverage, Coca-Cola currently offers nearly 400 brands in over 200 countries or territories and serves 1.5 billion servings each day.[2]
The company operates a franchised distribution system dating back to 1889 where The Coca-Cola Company only produces syrup concentrate which is then sold to various bottlers throughout the world who hold an exclusive territory.
The Coca-Cola Company is headquartered in Atlanta, Georgia. Its stock is listed on the NYSE and is part of DJIA and S&P 500. Its current president and CEO is Muhtar Kent.

2. IBM – The character of a company — the stamp it puts on its products, services and the marketplace — is shaped and defined over time. It evolves. It deepens. It is expressed in an ever-changing corporate culture, in transformational strategies, and in new and compelling offerings for customers. IBM’s character has been formed over nearly 100 years of doing business in the field of information-handling. Nearly all of the company’s products were designed and developed to record, process, communicate, store and retrieve information — from its first scales, tabulators and clocks to today’s powerful computers and vast global networks.
IBM helped pioneer information technology over the years, and it stands today at the forefront of a worldwide industry that is revolutionizing the way in which enterprises, organizations and people operate and thrive.
The pace of change in that industry, of course, is accelerating, and its scope and impact are widening. In these pages, you can trace that change from the earliest antecedents of IBM, to the most recent developments. You can scan the entire IBM continuum from the 19th century to the 21st or pinpoint — year-by year or decade-by-decade — the key events that have led to the IBM of today. We hope that you enjoy this unique look back at the highly textured history of the International Business Machines Corporation.

3. Microsoft – Microsoft Corporation is an American multinational computer technology corporation, which rose to dominate the home computer operating system market with MS-DOS in the mid-1980s, followed by the Windows line of operating systems.
Throughout its history the company has been the target of criticism for various reasons, including monopoly status and anti-competitive business practices including refusal to deal and tying. The U.S. Justice Department and the European Commission, among others, have ruled against Microsoft for various antitrust violations.[8][9]
It develops, manufactures, licenses, and supports a wide range of software products for computing devices.[10][7] Microsoft’s best-selling products are the Microsoft Windows operating system and the Microsoft Office suite of productivity software.

4. GE – The General Electric Company, or GE (NYSE: GE) is a multinational American technology and services conglomerate incorporated in the State of New York.[5] In terms of market capitalization as of 30th June 2008, GE is the world’s sixth largest company and also second in the BrandZ ranking. In the 1960s, aspects of U.S. tax laws and accounting practices led to a rise in the assembly of conglomerates. GE, which was a conglomerate long before the term was coined, is arguably the most successful organization of this type.

5. Nokia – Nokia is a Finnish multinational communications corporation, headquartered in Keilaniemi, Espoo, a city neighbouring Finland’s capital Helsinki. Nokia is focused on wireless and wired telecommunications, with 112,262 employees in 120 countries, sales in more than 150 countries and global annual revenue of 51.1 billion euros and operating profit of 8.0 billion as of 2007.[1][3] It is the world’s largest manufacturer of mobile telephones: its global device market share was about 40% in Q2 of 2008, up from 38% in Q2 2007 and up from 39% sequentially.[2] Nokia produces mobile phones for every major market segment and protocol, including GSM, CDMA, and W-CDMA (UMTS). Nokia’s subsidiary Nokia Siemens Networks produces telecommunications network equipments, solutions and services.

6. Toyota – Toyota Motor Corporation (トヨタ自動車株式会社, Toyota Jidosha Kabushiki-gaisha?) (pronounced [to-yo-ta]) is a multinational corporation headquartered in Japan, and is currently the world’s largest automaker.[3][4]
In 1934, while still a department of Toyota Industries, it created its first product Type A engine and in 1936 its first passenger car the Toyota AA. The company was eventually founded by Kiichiro Toyoda in 1937 as a spinoff from his father’s company Toyota Industries to create automobiles. Toyota currently owns and operates Lexus and Scion brands and has a majority shareholding stake in Daihatsu Motors,[5] and minority shareholdings in Fuji Heavy Industries Isuzu Motors, and Yamaha Motors. The company includes 522 subsidiaries.[6]
Toyota is headquartered in Aichi, Nagoya and in Tokyo. In addition to manufacturing automobiles, Toyota provides financial services through its division Toyota Financial Services and also creates robots. Toyota Industries and Finance divisions form the bulk of the Toyota Group, one of the largest conglomerates in the world

7. Intel – Intel Corporation (NASDAQ: INTC; SEHK: 4335) is the world’s second largest semiconductor company and the inventor of the x86 series of microprocessors, the processors found in most personal computers. Founded on July 18, 1968 as Integrated Electronics Corporation and based in Santa Clara, California, USA, Intel also makes motherboard chipsets, network cards and ICs, flash memory, graphic chips, embedded processors, and other devices related to communications and computing. Founded by semiconductor pioneers Robert Noyce and Gordon Moore, and widely associated with the executive leadership and vision of Andrew Grove, Intel combines advanced chip design capability with a leading-edge manufacturing capability. Originally known primarily to engineers and technologists, Intel’s successful “Intel Inside” advertising campaign of the 1990s made it and its Pentium processor household names.
Intel was an early developer of SRAM and DRAM memory chips, and this represented the majority of its business until the early 1980s. While Intel created the first commercial microprocessor chip in 1971, it was not until the success of the personal computer (PC) that this became their primary business. During the 1990s, Intel invested heavily in new microprocessor designs and in fostering the rapid growth of the PC industry. During this period Intel became the dominant supplier of microprocessors for PCs, and was known for aggressive and sometimes controversial tactics in defense of its market position, as well as a struggle with Microsoft for control over the direction of the PC industry.[5][6] The 2007 rankings of the world’s 100 most powerful brands published by Millward Brown Optimor showed the company’s brand value falling 10 places – from number 15 to number 25.[7]

8. McDonald’s – McDonald’s Corporation (NYSE: MCD) is the world’s largest chain of fast food restaurants, serving nearly 47 million customers daily.[3] McDonald’s primarily sells hamburgers, cheeseburgers, chicken products, French fries, breakfast items, soft drinks, milkshakes and desserts. More recently, it has begun to offer salads, wraps and fruit. Many McDonald’s restaurants have included a playground for children and advertising geared toward children, and some have been redesigned in a more ‘natural’ style, with a particular emphasis on comfort: introducing lounge areas and fireplaces, and eliminating hard plastic chairs and tables.
In addition to its signature restaurant chain, McDonald’s Corporation holds minority interest in Pret A Manger (a UK-based sandwich retailer), and owned the Chipotle Mexican Grill until 2006 and the restaurant chain Boston Market until 2007.[4] The company has also expanded the McDonald’s menu in recent decades to include alternative meal options, such as salads and snack wraps, in order to capitalize on growing consumer interest in health and wellness.
Each McDonald’s restaurant is operated by a franchisee, an affiliate, or the corporation itself. The corporations’ revenues come from the rent, royalties and fees paid by the franchisees, as well as sales in company-operated restaurants. McDonald’s revenues grew 27% over the three years ending in 2007 to $22.8 billion, and 9% growth in operating income to $3.9 billion.[5]

9. Disney – The Walt Disney Company (NYSE: DIS) is one of the largest media and entertainment corporations in the world. Founded on October 16, 1923, by brothers Walt and Roy Disney as an animation studio, it has become one of the biggest Hollywood studios, and owner of eleven theme parks and several television networks, including ABC and ESPN. Disney’s corporate headquarters and primary production facilities are located at The Walt Disney Studios in Burbank, California. The company is a component of the Dow Jones Industrial Average.

10. Google – Google Inc. is an American public corporation, earning revenue from advertising related to its Internet search, e-mail, online mapping, office productivity, social networking, and video sharing services as well as selling advertising-free versions of the same technologies. The Google headquarters, the Googleplex, is located in Mountain View, California. As of 30 June 2008 the company has 19,604 full-time employees.[

Seven Building Blocks of a Destination Website-Trust & Creditability

Seven Building Blocks of a Destination Website-Trust & Creditability:
The initial six building blocks in creating a Destination Website; proficient information, usability, website design, distinctive value proposition, time and presence, and voice are all things that we, more or less, have straight control over. The exemption is time. We don’t control time but we do control how we build up our presence over time.
Trust and credibility are also partly in our control but also two of the hardest things to attain. We find out whether we move forward in a reliable way, and whether or not to act in a plausible manner, but no matter how hard we try, we cannot wish those two things into existence. We cannot force someone else to believe us. We cannot tell someone to find us plausible and expect them to do so on our word.
We can go about doing all we can to build both trust and credibility, but, in the end, whether we are trusted or not lie not with our individual efforts but other people’s perceptions. If you spend months and years showing you can be trusted and proving that you’re plausible, but one knows or believes it to be true, then you just aren’t. These are not physical things that can be touched; they simply must be understood to be factual.
How to build trust and credibility:
Answer phone calls and return emails:
I’m astounded at how often I run across businesses that don’t do this. You would think that this is one of the fundamental no-brainers of doing business. Heck, if you can’t return a call or reply to an email, what signals are you sending to the prospective customer? First question: are you a lawful business? Second question: if I have a problem, which’s going to be there to help me out?
It’s bad enough that prospective customers call and get a voice mail during business hours. Shoddier when those calls are not returned. Rule of thumb, you have about 24 hours to reply to messages and emails before your trustworthiness is questioned. However, if you really want the customer, you should reply much faster. Twenty-four hours is a long time on the web and if you wait too long, you very soon might have lost them to a competitor.
Keep information secure:
Security is vital to conveying trust. Whether you are selling products or just capturing leads, visitors need to know that their information is going to be kept safe and it won’t be used for nefarious purposes. Using trust symbols such as Thawte, Better Business Bureau, and HackerSafe can all provide further feelings of trust. Linking to solitude and security policies from your forms can help as well.
Open communication:
Keeping communication open between you and your clients is crucial. This is more than just returning calls, but its dynamic participation. Both in meeting customer’s needs but also in foreseeing them. It means finding where your audience is and engaging with them in discussion, chat rooms, blogs and the like. Keeping communication open gives you opportunity to be truthful with your shortfalls, own up to your mistakes, and to present yourself as you truly are, a real person who cares genuinely about the needs of your audience.
Put the customer first:
We’ve all heard it said that “the consumer is always right.” Now I don’t certainly believe that’s true in all situations, but the point is, to survive in a consumer oriented business, we have to put the customer first. This means going out of your way to make certain the customer is satisfied with their purchase and transaction and if not, finding out what areas they are displeased in and provide a solution to make them satisfied.
Exceed expectations:
One of the best ways to build hope and trustworthiness is to simply exceed the expectations of your audience. This can be both easy and hard. It’s easy to find little ways to go the extra mile. To give a little extra service or extra benefit. It can be difficult, however, if you over-sell yourself. If you do that then you make it hard enough just to meet expectations. Look for opportunities to do something your clients or prospects don’t expect. Ways to prove to your customers that they are special to you.
Of course, all this isn’t just about building perceptions, but proving those perceptions to be factual. Creating a perception of trust, only to have it proven fake is far worse then never having built the aura of trust to begin with. If you fool visitors into thinking you’re plausible, they’ll soon find out you’re not. Both are difficult to rebuild than to build in the first place.
Putting them all together:
When building a Destination Website, all six other building blocks can be in place, but without this seventh one the first six are futile. Usability, voice, design, expert information, etc., all just become part of the hoax. But, if you are really building up trust that can be trusted and credibility that is credible, the first six building blocks all lend a hand to that end. They all play a role at helping to establish and prove your credibility.
Very unbeaten businesses, both on-and offline have been built on this last building block alone. In fact, only this last one is necessary for success, though all seven are essential to build a Destination Website. Like any good foundation, all seven building blocks provide support for the other six, with faith and credibility being the most vital piece of the pie.

SEO and flash never go together

We frequently deal with clients that are planning to “revamp” their sites with Flash, with SEO having already generated incredible gains in their sales. The thing that we most dread to hear is that they’ve hired a skilled “Flash designer” that will be taking their websites to the “next level.” unluckily, that “next level” is often the basement – at least in terms of SEO results.
The bottom line here is that a site built entirely in Flash still faces huge hindrances. While there have been current moves from Google and Yahoo! to try to index the content from pooled Flash/SEO sites, those moves have not yet, from my experience, translated into SEO results or success (at least when compared to html sites).
We should make a difference here between embedded Flash and sites built entirely from Flash. For instance, a site that has Flash elements but still contains basic html elements will not overly suffer, as the Flash element (usually a movie in a box on the homepage or elsewhere) is externalized. A search engine spider will generally not try to parse through any files that have been externalized in the code – they will only index the code that is readily evident on the source page.
On the other hand, from an SEO results perspective, there are still major issues with sites that are built entirely in Flash, and SEO is in general the first thing that suffers. First of all, the URL normally never changes no matter where people navigate on the site. As any well-mannered SEO practitioner will tell you, every page of your site is a potential entry page for a search engine. With a site built in Flash, SEO suffers even more as you only have one potential entry page, which is the main URL. This cuts off dozens, hundreds, or thousands of potential pages that could be indexed in Google and Yahoo! (and all other engines). When your only prospective entry page in the search engine listings is your home page, it is quite hard to target a wide assortment of key phrases, potentially eliminating SEO results or rankings.
Content is another very big issue. Search engines rank pages based upon a number of criteria, but one of the most significant to SEO results is the text that they can “understand” on individual pages. At present, search engines read first and foremost html text (although some also read text in the PDF format) – which means that if you decide that you want to use a uncommon and fancy font that must be displayed in graphic form the engine will not read the text and therefore will not know what the page is about, which could harm SEO results. Naturally, this also includes any of the text included in Flash. While Yahoo! and Google have just announced improved capabilities in reading content within Flash, I have not personally seen that translate into great SEO results for competitive key phrases.
One other emerging feature is that as search evolves, more and more people are looking for information while they are away from their computers. Many mobile devices are currently inept of displaying Flash content, although recent moves by Adobe to make “Flash Lite” available may change this. However, it remains to be seen whether people that are seeking information on a mobile device will even want to find the way through Flash, especially if they can get the information that they seek from a fast-loading html page. In my view, lean html content will be at a premium when a company is trying to target a mobile audience.
In spite of the difficulties, it is not the intent of this article to declare that Flash and SEO will always be incompatible – merely that it is the state of the current situation. You can find many differing opinions on mixing Flash and SEO on the internet, but the true test is to try to find a Flash site (that is to say, a site built entirely in Flash) that you esteem and see if it ranks well in SEO results for 50+ competitive terms that are associated to the specific business (in Google or Yahoo!). In my experience, such sites that combine Flash and SEO are nearly not possible to find.
Flash can be, and repeatedly is, used for great effect on the internet, in interactive kiosks, and in further applications. I’m not from the “any Flash is bad” school, although I do think that many Flash practitioners tend to get a little carried away and often disregard basic usability issues. However, sites built entirely in Flash with SEO elements are still, again in my opinion, like oil and water – Flash and SEO are evidently alone useful, but they don’t mix well. Until they do, I will continue to advise my clients not to build sites completely out of Flash – or, at the very least, to have an alternate html option for search engine and user preference purposes. At the end of the day, many clients are astonished to find out how many visitors actually prefer “old school” html.