Home Depot Product Authority, LLC

Ali Ahmadvand

Surya Kallumadi

Faizan Javed

Khalifeh Al Jadda

Eugene Agichtein

Simon Hughes

Sayyed M Zahiri

A method for determining a category responsive to a user query is disclosed. The method includes receiving a training data set comprising a plurality of data pairs, each data pair including: (i) a query; and (ii) an associated one or more categories that are responsive to the query, wherein the one or more categories in the training data set defines a plurality of categories. The method includes training a machine learning algorithm, according to the training data set, to create a trained model, wherein training the machine learning algorithm includes: creating a first co-occurrence data structure defining co-occurrence of respective word representations of the queries with the plurality of categories, and creating a second co-occurrence data structure defining co-occurrence of respective categories in respective data pairs. The method also includes deploying the trained model to return one or more categories in response to a new query input.

Responsive category prediction for user queries

A method of providing results from a search engine comprises generating an initial set based on a query received from a user; generating an attention value based on the query and on the initial set; applying the attention value to the initial set; and presenting a set of results in response to the query, wherein the results are generated according to the application of the attention value to the initial set.

Attentive pseudo-relevance feedback network for query categorization

A method of configuring a search engine to classify a search query includes receiving a search query data set, the search query data set comprising a plurality of search queries, defining a first set of candidate labels and a second set of candidate labels according to the search queries in the search query data set, concatenating the first set of candidate labels with the second set of candidate labels to generate a concatenated candidate label set, generating a compatibility matrix comprising a similarity between the concatenated candidate label set and the search query data set, and training a classification network according to the compatibility matrix.

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Atlanta, GA, United States of America

Ali Ahmadvand