Yahoo! Inc.

Overture Services, Inc.

Daniel C Fain

Peter Savich

Paul T Ryan

Jianchang Mao

Thomas W Pierce

Rosemary Ellen Jones

John Joseph M Carrasco

Gary William Flake

Jacob Sisk

Heidi Eldenburg Bramlet

Charity A Rieck

Described herein are methods for creating categorized documents, categorizing documents in a distributed database and categorizing Resulting Pages. Also described herein is an apparatus for searching a distributed database. The method for creating categorized documents generally comprises: initially assuming all documents are of type 1; filtering out all type 2 documents and placing them in a first category; filtering out all type 3 documents and placing them in a second category; and defining all remaining documents as type 4 documents and placing all type 4 documents in a third category. The apparatus for searching a distributed database generally comprises at least one memory device; a computing apparatus; an indexer; a transactional score generator; and a category assignor; a search server; and a user interface in communication with the search server.

Method and apparatus for categorizing and presenting documents of a distributed database

The present invention provides a method and system for identifying terms in a requested Web document which can be used to identify other web page documents in which the user is likely to have an interest. Terms on previously prepared lists are compared to the content of the Web page document, and where matches are found, mathematical techniques including linear regression can be used to determine the relevance of the textual term to the content of the page. This can be used to suggest other linked Web page documents in which the user is likely to have an interest. Where the prepared lists include a term that has been purchased by advertisers, and there is a match for that term on the Web page document, an ad which is likely to interest the reader can be displayed on the requested Web page document, and can include a link to the advertiser's web site.

System and methods for indentifying the potential advertising value of terms found on web pages

Various embodiments are directed to a system and method providing associative matching of terms. Candidate terms are selected for building one or more associative matching models from one or more selected candidate sources. Associativity is defined to give editors the ability to label sample associative term pairs from the one or more candidate sources. The editors label sample candidate term pairs as being related. Features are determined that can differentiate associative from non-associative pairs. The selected features are used to build a model. The model is applied to determine whether a received query-candidate pair are associative.

Term expansion using associative matching of labeled term pairs

The likely relevance of each term of a search-engine query of two or more terms is determined by their deletion probability scores. If the deletion probability scores are significantly different, the deletion probability score can be used to return targeted ads related to the more relevant term or terms along with the search results. Deletion probability scores are determined by first gathering historical records of search queries of two or more terms in which a subsequent query was submitted by the same user after one or more of the terms had been deleted. The deletion probability score for a particular term of a search query is calculated as the ratio of the number of times that particular term was itself deleted prior to a subsequent search by the same user divided by the number of times there were subsequent search queries by the same user in which any term or terms including that given term was deleted by the same user prior to the subsequent search. Terms are not limited to individual alphabetic words.

System and methods for ranking the relative value of terms in a multi-term search query using deletion prediction

In one implementation a method for disambiguation of search phrases is provided, which may include identifying interpretation clusters using bidded search terms of content providers and influencing a position of a search result in a search results list based on the interpretation clusters. In one implementation a method is provided for interpretation clustering, which may include identifying terms and associated content providers and calculating a matrix from the identified terms and associated content providers. Similarity scores between content providers based on the matrix are calculated and interpretation clusters are assigned using the similarity scores. Bidded search terms and/or searcher clickthrough terms may be used. In one implementation, a method is provided for increasing the relevance of search results, which may include receiving a search query and identifying the search query as ambiguous.

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Los Angeles, CA, United States of America

Daniel C Fain