Microsoft Technology Licensing, LLC

Emine Yilmaz

Paul Nathan Bennett

Nicholas Eric Craswell

Fernando David Diaz

Filip Andrzej Radlinski

David Anthony Hawking

Corbin Louis Rosset

Bhaskar Mitra

Described herein is a mechanism for utilizing a neural network to identify and rank search results. A machine learning model is trained by converting training data comprising query-document entries into query term-document entries. The query term-document entries are utilized to train the machine learning model. A set of query terms are identified. The query terms can be derived from a query history. The trained machine learning model is used to calculate document ranking scores for the query terms and the resultant scores are stored in a pre-calculated term-document index. A query to search the document index is broken down into its constituent terms and an aggregate document ranking score is calculated from a weighted sum of the document ranking scores corresponding to the individual query terms. Because the term-document index can be pre-calculated, it can be downloaded to provide deep learning search capabilities in a computationally limited environment.

Neural network for search retrieval and ranking

Measuring duplication in search results is described. In one example, duplication between a pair of results provided by an information retrieval system in response to a query is measured. History data for the information retrieval system is accessed and query data retrieved, which describes the number of times that users have previously selected either or both of the pair of results, and a relative presentation sequence of the pair of results when displayed at each selection. From the query data, a fraction of user selections is determined in which a predefined combination of one or both of the pair of results were selected for a predefined presentation sequence. From the fraction, a measure of duplication between the pair of results is found. In further examples, the information retrieval system uses the measure of duplication to determine an overall redundancy value for a result set, and controls the result display accordingly.

Measuring duplication in search results

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Cambridge, United Kingdom

Emine Yilmaz