Salesforce Com, Inc.

Salesforce, Inc.

Markus Anderle

Caiming Xiong

Jia Li

Harshavardhan Utharavalli

Chenxi Li

Richard Socher

Chu Hong Hoi

Wenzhuo Yang

Yongjun Chen

Latrice Barnett

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

Embodiments described herein provide an attentive network framework that models dynamic attributes with item and feature interactions. Specifically, the attentive network framework first encodes basket item sequences and dynamic attribute sequences with time-aware padding and time/month encoding to capture the seasonal patterns (e.g. in app recommendation, outdoor activities apps are more suitable for summer time while indoor activity apps are better for winter). Then the attentive network framework applies time-level attention modules on basket items' sequences and dynamic user attributes' sequences to capture basket items to basket items and attributes to attributes temporal sequential patterns. After that, an intra-basket attentive module is used on items in each basket to capture the correlation information among items.

Systems and methods for next basket recommendation with dynamic attributes modeling

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Moraga, CA, United States of America

Markus Anderle