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Diversity and Explainability Parameters for Recommendation Accuracy in Machine Learning Recommendation Systems

机译：机器学习推荐系统建议准确性的多样性和解释性参数

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摘要

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.

机译：实施例涉及机器学习推荐系统。该系统接收用户查询，用于为一个或多个项目生成推荐，其中具有与推荐一个或多个项目相关联的说明。该系统获得至少一个用户的第一特征和一组项目的第二特征。该系统向第一机器学习网络提供第一特征和第二特征，用于确定项目的预测分数。该系统提供第一特征的一部分和第二个特征的一部分到第二机器学习网络，用于确定项目的可解释性分数并生成相应的解释叙述。该系统基于排名预测分数和可解释项目的解释性分数，为一个或多个项目和相应的解释叙述提供了建议。

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公开/公告号US2021374132A1

专利类型
公开/公告日2021-12-02

原文格式PDF
申请/专利权人 SALESFORCE.COM INC.;
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申请/专利号US202017093885
发明设计人 WENZHUO YANG;JIA LI;CHENXI LI;LATRICE BARNETT;MARKUS ANDERLE;SIMO ARAJARVI;HARSHAVARDHAN UTHARAVALLI;CAIMING XIONG;RICHARD SOCHER;CHU HONG HOI;
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申请日2020-11-10
分类号G06F16/2457;G06N20/20;
国家 US
入库时间 2022-08-24 22:34:23

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