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.

Abstract: In a method of generating a search boost vector, a first set of content location identifiers for first content are provided. A second set of content location identifiers for second content are identified. The second content is related to the first content in accordance with one or more first categories corresponding to the first content. The one or more first categories may be determined by applying a text classifier to the first content. The second content may also be identified based on one or more user profiles and/or one or more content site profiles. The search boost vector is generated using weights corresponding to the content location identifiers in the first set of content location identifiers and/or the second set of content location identifiers. The one or more search boost vectors may be used to boost one or more search results.

Abstract: A search engine identifies a list of documents from a set of documents in a database in response to a set of query terms. For each document in the list, the search engine determines an information retrieval score based on its content and the query terms, and also identifies a set of source documents that have links to the document and that also have anchor text satisfying a predefined requirement with respect to the query terms. The search engine calculates a personalized page importance score for each of the identified source documents according to a set of user-specific parameters and accumulates the personalized page importance scores to produce a personalized anchor text score for the document. The personalized anchor text score is then combined with the document's information retrieval score to generate a personalized ranking for the document. The documents are ordered according to their respective personalized rankings.

Abstract: Techniques for assigning ranks to nodes in a large linked database, such as world wide web or any other hypermedia database, partition the nodes so that the link matrix has a predominantly block-diagonal form. Within each block, a local rank is computed for nodes in the block, possibly by different computer in a distributed computing environment. A block rank is then estimated for each block as a whole, and may optionally include block-level weights to implement customized ranking. The local ranks and block ranks are then combined to form a global rank, which may be used to rank the nodes. Alternatively, a global rank vector for the database may be used as an initial vector in an iterative link-based ranking scheme to obtain more accurate global ranks for the nodes. The global rank vector may be divided to provide local rank vectors for use in subsequent applications of the method.

Abstract: A system and method is disclosed in which a ranking function for a set of document rank values is iteratively solved with respect to a set of linked documents until a first stability condition is satisfied. After such condition is satisfied, some of the ranks will have converged. The ranking function is modified to take into account these converged ranks so as to reduce the ranking function's computation cost. The modified ranking function is then solved until a second stability condition is satisfied. After such condition is satisfied more of the ranks will have converged. The ranking function is again modified and process continues until complete.