Asymmetrically hierarchical networks with attentive interactions for interpretable review-based recommendation

    公开(公告)号:US11521255B2

    公开(公告)日:2022-12-06

    申请号:US16995052

    申请日:2020-08-17

    IPC分类号: G06Q30/00 G06Q30/06 G06N20/00

    摘要: A method for implementing a recommendation system using an asymmetrically hierarchical network includes, for a user and an item corresponding to a user-item pair, aggregating, using asymmetrically designed sentence aggregators, respective ones of a set of item sentence embeddings and a set of user sentence embeddings to generate a set of item review embeddings based on first item attention weights and a set of user review embeddings based on first user attention weights, respectively, aggregating, using asymmetrically designed review aggregators, respective ones of the set of item review embeddings and the set of user review embeddings to generate an item embedding based on a second item attention weights and a user embedding based on second user attention weights, respectively, and predicting a rating of the user-item pair based on the item embedding and the user embedding.

    ASYMMETRICALLY HIERARCHICAL NETWORKS WITH ATTENTIVE INTERACTIONS FOR INTERPRETABLE REVIEW-BASED RECOMMENDATION

    公开(公告)号:US20210065278A1

    公开(公告)日:2021-03-04

    申请号:US16995052

    申请日:2020-08-17

    IPC分类号: G06Q30/06 G06N20/00

    摘要: A method for implementing a recommendation system using an asymmetrically hierarchical network includes, for a user and an item corresponding to a user-item pair, aggregating, using asymmetrically designed sentence aggregators, respective ones of a set of item sentence embeddings and a set of user sentence embeddings to generate a set of item review embeddings based on first item attention weights and a set of user review embeddings based on first user attention weights, respectively, aggregating, using asymmetrically designed review aggregators, respective ones of the set of item review embeddings and the set of user review embeddings to generate an item embedding based on a second item attention weights and a user embedding based on second user attention weights, respectively, and predicting a rating of the user-item pair based on the item embedding and the user embedding.