Abstract: A method and system is provided for measuring a user preference by obtaining information representing a length of time that a user hovers a user-controlled pointer over a display region corresponding to a first search result without selecting the first search result. A second search result is provided in accordance with the user preference. In some instances additional information is provided concerning the first search result over which the user-controlled pointer is hovering. In some embodiments the first search result comprises a portion that is a snippet and the obtaining information representing the length of time that a user hovers a user-controlled pointer over a display region comprises determining an amount of time that a user hovers a user-controlled pointer over the snippet.

Abstract: Snippets of text provided are generated based in part on a user's profile. An item, such as a document, is examined to identify terms related to the user's profile. A term profile for an identified term is compared to a user's profile. The more closely related the identified term is to the user's profile, the higher a similarity score will be. Alternatively, terms found in a document may have a user profile score which may be obtained by looking the term up in the user's profile. Terms having high profile similarity scores or high user profile scores are used in identifying snippets which may be relevant to a user. The high scoring terms may be added to search terms and provided to a snippet generator.

Abstract: A method and system of modulating search result relevancy use various types of user browsing activities. In particular, a client assistant residing in a client computer monitors movements of a user controlled pointer in a web browser, e.g., when the pointer moves into a predefined region and when it moves out of the predefined region. A server then determines a relevancy value between an informational item associated with the predefined region and a search query according to the pointer hover period. When preparing a new search result responsive to a search query, the server re-orders identified informational items in accordance with their respective relevancy values such that more relevant items appear before less relevant ones. The server also uses the relevancy values to determine and/or adjust the content of an one-box result associated with a search query.

Abstract: A set of ordered predicted completion strings are presented to a user as the user enters text in a text entry box (e.g., a browser or a toolbar). The predicted completion strings can be in the form of URLs or query strings. The ordering may be based on any number of factors (e.g., a query's frequency of submission from a community of users). URLs can be ranked based on an importance value of the URL. Privacy is taken into account in a number of ways, such as using a previously submitted query only when more than a certain number of unique requestors have made the query. The sets of ordered predicted completion strings is obtained by matching a fingerprint value of the user's entry string to a fingerprint to table map which contains the set of ordered predicted completion strings.

Abstract: A search engine provides personalized rankings of search results. A user interest profile identifies topics of interest to a user. Each topic is associated with one or more sites, and a boost value, which can be used to augment an information retrieval score of any document from the site. Search results from any search are provided to the user, with a variable control of the ranking of the results. The results can be ranked by their unboosted information retrieval score, thus reflecting no personalization, or by their fully or partially boosted information retrieval scores. This allows the user to selectively control how their interests affect the ranking of the documents.

Abstract: Targeted advertisements are provided to an advertisement consumer based on a user profile, a page profile, or a combination thereof. In embodiment where a user of a search engine is an advertisement consumer, the user utilizes a search engine to obtain search results relevant to a search query. A user profile of the user's interests is used to select advertisements for inclusion with search results. The user profile is evaluated by an advertisement server which determines which advertiser(s) offers a highest price for the user profile. Advertisements from these advertisers are then selected. In another embodiment, where the user is accessing a page on a third party website, the page may include a request for advertisements. A page profile is evaluated by an advertisement server that determines which advertiser(s) offers a highest price for the page profile. Advertisements from these advertisers are then selected, and provided to the user, where they are included in the retrieved page.

Abstract: A search system monitors the input of a search query by a user. Before the user finishes entering the search query, the search system identifies and sends a portion of the query as a partial query to the search engine. Based on the partial query, the search engine creates a set of predicted queries. This process may take into account prior queries submitted by a community of users, and may take into account a user profile. The predicted queries are to be sent back to the user for possible selection. The search system may also cache search results corresponding to one or more of the predicted queries in anticipation of the user selecting one of the predicted queries. The search engine may also return at least a portion of the search results corresponding to one or more of the predicted queries.

Abstract: A search engine provides personalized rankings of search results. A user interest profile identifies topics of interest to a user. Each topic is associated with one or more sites, and a boost value, which can be used to augment an information retrieval score of any document from the site. Search results from any search are provided to the user, with a variable control of the ranking of the results. The results can be ranked by their unboosted information retrieval score, thus reflecting no personalization, or by their fully or partially boosted information retrieval scores. This allows the user to selectively control how their interests affect the ranking of the documents.

Abstract: A computer-implemented method identifies nodes that are beneficiaries of node importance inflating links in a directed graph of linked nodes. The directed graph of linked nodes corresponds to a linked database, and the nodes correspond to documents within the linked database. The method is performed by a computer system including one or more processors and memory storing one or more programs, the one or more processors executing the one or more programs to perform the method. The method includes computing, for each of at least a subset of the nodes in the directed graph, a respective quantity corresponding to a mathematical derivative of a node importance function, and performing a remedial action on a respective node in the directed graph in accordance with the respective computed quantity computed for the respective node.

Abstract: A search engine provides personalized rankings of search results. A user interest profile identifies topics of interest to a user. Each topic is associated with one or more sites, and a boost value, which can be used to augment an information retrieval score of any document from the site. Search results from any search are provided to the user, with a variable control of the ranking of the results. The results can be ranked by their unboosted information retrieval score, thus reflecting no personalization, or by their fully or partially boosted information retrieval scores. This allows the user to selectively control how their interests affect the ranking of the documents.

Abstract: A method of processing information related to documents in a collection of linked documents. For each respective document in all or a portion of said collection, one or more auxiliary page ranking vectors associated with the respective document are quantized. A search query comprising one or more search terms is received. Using a document index that represents said collection of linked documents, a plurality of documents is identified. Each document in the identified plurality of documents includes at least one term that matches a search term in the search query. For one or more respective documents in the plurality of documents, one or more of the auxiliary page ranking vectors associated with the respective document are decoded. The plurality of documents are then ranked using the decoded auxiliary page vectors.

Abstract: In a method of profiling a website, an information server receives multiple search queries from a website submitted by different users. Different search results responsive to the search queries are provided to the requesting users. The information server monitors activities of the users on the search results and generates a profile for the website using the search queries and the user activities. When the information server receives a same search query from two different websites, it identifies a plurality of information items associated with the search query. The information server uses profiles of the two websites to customize the information items into two different orders and serves the information items to the two websites in the two different orders.

Abstract: A search system monitors the input of a search query by a user. Before the user finishes entering the search query, the search system identifies and sends a portion of the query as a partial query to the search engine. Based on the partial query, the search engine creates a set of predicted queries. This process may take into account prior queries submitted by a community of users, and may take into account a user profile. The predicted queries are be sent back to the user for possible selection. The search system may also cache search results corresponding to one or more of the predicted queries in anticipation of the user selecting one of the predicted queries. The search engine may also return at least a portion of the search results corresponding to one or more of the predicted queries.

Abstract: A set of ordered predicted completion strings are presented to a user as the user enters text in a text entry box (e.g., a browser or a toolbar). The predicted completion strings can be in the form of URLs or query strings. The ordering may be based on any number of factors (e.g., a query's frequency of submission from a community of users). URLs can be ranked based on an importance value of the URL. Privacy is taken into account in a number of ways, such as using a previously submitted query only when more than a certain number of unique requestors have made the query. The sets of ordered predicted completion strings is obtained by matching a fingerprint value of the user's entry string to a fingerprint to table map which contains the set of ordered predicted completion strings.

Abstract: In a method of profiling a website, an information server receives multiple search queries from a website submitted by different users. Different search results responsive to the search queries are provided to the requesting users. The information server monitors activities of the users on the search results and generates a profile for the website using the search queries and the user activities. When the information server receives a same search query from two different websites, it identifies a plurality of information items associated with the search query. The information server uses profiles of the two websites to customize the information items into two different orders and serves the information items to the two websites in the two different orders.

Abstract: A computer-implemented method identifies nodes that are beneficiaries of node importance inflating links in a directed graph of linked nodes. The directed graph of linked nodes corresponds to a linked database, and the nodes correspond to documents within the linked database. The method is performed by a computer system including one or more processors and memory storing one or more programs, the one or more processors executing the one or more programs to perform the method. The method includes computing, for each of at least a subset of the nodes in the directed graph, a respective quantity corresponding to a mathematical derivative of a node importance function, and performing a remedial action on a respective node in the directed graph in accordance with the respective computed quantity computed for the respective node.

Abstract: A set of ordered predicted completion strings are presented to a user as the user enters text in a text entry box (e.g., a browser or a toolbar). The predicted completion strings can be in the form of URLs or query strings. The ordering may be based on any number of factors (e.g., a query's frequency of submission from a community of users). URLs can be ranked based on an importance value of the URL. Privacy is taken into account in a number of ways, such as using a previously submitted query only when more than a certain number of unique requestors have made the query. The sets of ordered predicted completion strings is obtained by matching a fingerprint value of the user's entry string to a fingerprint to table map which contains the set of ordered predicted completion strings.

Abstract: Methods for facilitating the identification of link spamming in a linked database include calculating a spam likelihood value for nodes in a directed graph of linked nodes are disclosed. The spam likelihood value is computed from an importance of the node and a derivative value of the importance function with respect to a coupling factor. The likelihood that the node's importance is inflated by link spam is estimated by calculating the ratio of the magnitude of the derivative value for the node to the rank for the node. Alternatively, the spam likelihood may be computed directly from a component of the principal eigenvector of A evaluated at two values of the parameter c. The normalized derivative value can also be used to provide an order of importance in a list of nodes.

Abstract: A search system monitors the input of a search query by a user. Before the user finishes entering the search query, the search system identifies and sends a portion of the query as a partial query to the search engine. Based on the partial query, the search engine creates a set of predicted queries. This process may take into account prior queries submitted by a community of users, and may take into account a user profile. The predicted queries are be sent back to the user for possible selection. The search system may also cache search results corresponding to one or more of the predicted queries in anticipation of the user selecting one of the predicted queries. The search engine may also return at least a portion of the search results corresponding to one or more of the predicted queries.

Abstract: Targeted advertisements are provided to an advertisement consumer based on a user profile, a page profile, or a combination thereof. In embodiment where a user of a search engine is an advertisement consumer, the user utilizes a search engine to obtain search results relevant to a search query. A user profile of the user's interests is used to select advertisements for inclusion with search results. The user profile is evaluated by an advertisement server which determines which advertiser(s) offers a highest price for the user profile. Advertisements from these advertisers are then selected. In another embodiment, where the user is accessing a page on a third party website, the page may include a request for advertisements. A page profile is evaluated by an advertisement server that determines which advertiser(s) offers a highest price for the page profile. Advertisements from these advertisers are then selected, and provided to the user, where they are included in the retrieved page.