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Naveen Krishna

Rongkai Zhao

Ravi Sambhu

Rajdeep Mondal

Jared Moore

Methods of operating a search engine may include calculating multi-modal document vector models for each of a plurality of electronic documents, training category-specific, search query-specific ranking models with respective machine learning algorithms based on those document vector models, and applying each of those models to further instances of the same search query to rank the documents responsive to that search query.

Ranking and presenting search engine results based on category-specific ranking models

A method for operating a search engine may include determining a multi-dimensional search parameter space comprising a set of possible weight values for each of a plurality of search parameters and dividing the search parameter space into a grid of evenly-spaced values that is a subset of the set of possible values. The method may further include defining one or more initial populations of search parameter weight values, wherein each population of search parameter weight values comprises a plurality of initial individuals, wherein each initial individual comprises a respective one of the evenly-spaced values for each of the search parameters. The method may further include executing one or more genetic algorithms based on the one or more initial populations to select a final set of search parameter weight values, and returning results of a user search in the search engine according to the final set of search parameter weight values.

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Naveen Krishna