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 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: Systems, methods and program products for classifying text. A system classifies text into first subject matter categories. The system identifies one or more second subject matter categories in a collection of second subject matter categories, each of the second categories is a hierarchical classification of a collection of confirmed valid search results for queries, in which at least one query for each identified second category includes a term in the text. The system filters the identified categories by excluding identified categories whose ancestors are not among the first categories. The system selects categories from the filtered categories based on one or more thresholds in which a threshold specifies a degree of relatedness between a selected category and the text. The selected categories are a sufficient basis for recommending content to a user, the content being associated with one or more of the selected categories.

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 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.

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 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 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: 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 third party website provides a search interface to a general search engine. A site profile of the third party website describes various topics, keywords, or domains that are potentially relevant or of interest to users who access the third party website. The topics are associated with a topical directory, with domains associated with each topic; the domains in a given topic are given various weightings. When a search is submitted to the general search engine from the third party website via the search interface, the general search engine uses the site profile to customize the search results. The search results are customized by weighting the ranking of documents from websites associated with the topics in the site profile. The site profile can be manually or automatically constructed.

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 requesters 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 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.