Abstract: A method and apparatus for using network traffic logs for search enhancement is disclosed. According to one embodiment, network usage is tracked by generating log files. These log files among other things indicate the frequency web pages are referenced and modified. These log files or information from these log files can then be used to improve document ranking, improve web crawling, determine tiers in a multi-tiered index, determine where to insert a document in a multi-tiered index, determine link weights, and update a search engine index.

Abstract: A method and apparatus for determining a ranking function by regression using relative preference data. A number of iterations are performed in which to following is performed. The current ranking function is used to compare pairs of elements. The comparisons are checked against actual preference data to determine for which pairs the ranking function mis-predicted (contradicting pairs). A regression function is fitted to a set of training data that is based on contradicting pairs and a target value for each element. The target value for each element may be based on the value that the ranking function predicted for the other element in the pair. The ranking function for the next iteration is determined based, at least in part, on the regression function. The final ranking function is established based on the regression functions. For example, the final ranking function may be based on a linear combination of regression functions.

Abstract: A method and apparatus for using network traffic logs for search enhancement is disclosed. According to one embodiment, network usage is tracked by generating log files. These log files among other things indicate the frequency web pages are referenced and modified. These log files or information from these log files can then be used to improve document ranking, improve web crawling, determine tiers in a multi-tiered index, determine where to insert a document in a multi-tiered index, determine link weights, and update a search engine index.

Abstract: The system and method of the present invention allows for the determination of the relevance of a content item to a query through the use of a machine learned relevance function that incorporate query differentiation. A method for selecting a relevance function to determine a relevance of a query-content item pair comprises generating a training set comprising one or more content item-query pairs. Content item-query pairs in the training set are collectively used to determine the relevance function by minimizing a loss function according to a relevance score adjustment function that accounts for query differentiation. The monotocity of relevance score adjustment function allows the trained relevance function to be directly applied to new queries.

Abstract: A method and apparatus for associating documents with classification values and ranking documents based on classification weights is provided. It is determined if a document is associated a classification. If the document is associated with a classification, then it is determined if a classification value, which is associated with the document, is associated with a weight. If the classification value is associated with a weight, then a rank of the document is adjusted based on the weight that is associated with the classification value.

Abstract: A method and apparatus for associating documents with classification values and ranking documents based on classification weights is provided. It is determined if a document is associated a classification. If the document is associated with a classification, then it is determined if a classification value, which is associated with the document, is associated with a weight. If the classification value is associated with a weight, then a rank of the document is adjusted based on the weight that is associated with the classification value.

Abstract: A method and apparatus are provided for determining when electronic documents stored in a large collection of documents are similar to one another. A plurality of similarity information is derived from the documents. The similarity information may be based on a variety of factors, including hyperlinks in the documents, text similarity, user click-through information, similarity in the titles of the documents or their location identifiers, and patterns of user viewing. The similarity information is fed to a combination function that synthesizes the various measures of similarity information into combined similarity information. Using the combined similarity information, an objective function is iteratively maximized in order to yield a generalized similarity value that expresses the similarity of particular pairs of documents.