Some factoids about how the Google Algorithm works…

This document is several years old but most of the information is still relevant to SEO, though one factor or another may have increased/decreased in importance since it was published.

Google Search Factors:

1. Document Inception Date

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.

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.

[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.

Content Updates/Changes

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

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).

Query Analysis

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.

Link-Based Criteria

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).

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.

Anchor Text

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

Traffic

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.

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.

Domain-Related Information

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.

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.

User Maintained/Generated Data

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.

Unique Words, Bigrams, Phrases in Anchor Text

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.

Linkage of Independent Peers

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.