Google Inc.

Tessa Macduff

Taher H Haveliwala

Glen M Jeh

Sepandar D Kamvar

Bilgehan Uygar Oztekin

Mehran Sahami

Sugato Basu

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 predefined criteria. A search boost vector is generated by determining weights corresponding to content location identifiers in the second set of content location identifiers. The search boost vector may also be generated by determining weights corresponding to content location identifiers in the first set of content location identifiers. The predefined criteria may include one or more additional content location identifiers that are co-visited with one or more content location identifiers for the first set of content location identifiers. Co-visits are by a community of users during an interval of time.

Search boost vector based on co-visitation information

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.

Query boosting based on classification

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Redwood City, CA, United States of America

Tessa Macduff