Reinforcement knowledge graph reasoning for explainable recommendation

Yikun Xian, Zuohui Fu, S. Muthukrishnan, Gerard De Melo, Yongfeng Zhang

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Recent advances in personalized recommendation have sparked great interest in the exploitation of rich structured information provided by knowledge graphs. Unlike most existing approaches that only focus on leveraging knowledge graphs for more accurate recommendation, we perform explicit reasoning with knowledge for decision making so that the recommendations are generated and supported by an interpretable causal inference procedure. To this end, we propose a method called Policy-Guided Path Reasoning (PGPR), which couples recommendation and interpretability by providing actual paths in a knowledge graph. Our contributions include four aspects. We first highlight the significance of incorporating knowledge graphs into recommendation to formally define and interpret the reasoning process. Second, we propose a reinforcement learning (RL) approach featuring an innovative soft reward strategy, user-conditional action pruning and a multi-hop scoring function. Third, we design a policy-guided graph search algorithm to efficiently and effectively sample reasoning paths for recommendation. Finally, we extensively evaluate our method on several large-scale real-world benchmark datasets, obtaining favorable results compared with state-of-the-art methods.

    Original languageEnglish (US)
    Title of host publicationSIGIR 2019 - Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval
    PublisherAssociation for Computing Machinery, Inc
    Pages285-294
    Number of pages10
    ISBN (Electronic)9781450361729
    DOIs
    StatePublished - Jul 18 2019
    Event42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019 - Paris, France
    Duration: Jul 21 2019Jul 25 2019

    Publication series

    NameSIGIR 2019 - Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

    Conference

    Conference42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019
    CountryFrance
    CityParis
    Period7/21/197/25/19

    Keywords

    • Explainability
    • Knowledge Graphs
    • Recommendation System
    • Reinforcement Learning

    ASJC Scopus subject areas

    • Information Systems
    • Applied Mathematics
    • Software

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