Salesforce, Inc.

Latrice Barnett

Caiming Xiong

Richard Socher

Chu Hong Hoi

Jia Li

Wenzhuo Yang

Markus Anderle

Chenxi Li

Harshavardhan Utharavalli

Simo Arajarvi

Embodiments are directed to a machine learning recommendation system. The system receives a user query for generating a recommendation for one or more items with an explanation associated with recommending the one or more items. The system obtains first features of at least one user and second features of a set of items. The system provides the first features and the second features to a first machine learning network for determining a predicted score for an item. The system provides a portion of the first features and a portion of the second features to second machine learning networks for determining explainability scores for an item and generating corresponding explanation narratives. The system provides the recommendation for one or more items and corresponding explanation narratives based on ranking predicted scores and explainability scores for the items.

Diversity and explainability parameters for recommendation accuracy in machine learning recommendation systems

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Oakland, CA, United States of America

Latrice Barnett