CAFE: Coarse-to-Fine Neural Symbolic Reasoning for Explainable Recommendation

Yikun Xian, Zuohui Fu, Handong Zhao, Yingqiang Ge, Xu Chen, Qiaoying Huang, Shijie Geng, Zhou Qin, Gerard De Melo, S. Muthukrishnan, Yongfeng Zhang

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

    Abstract

    Recent research explores incorporating knowledge graphs (KG) into e-commerce recommender systems, not only to achieve better recommendation performance, but more importantly to generate explanations of why particular decisions are made. This can be achieved by explicit KG reasoning, where a model starts from a user node, sequentially determines the next step, and walks towards an item node of potential interest to the user. However, this is challenging due to the huge search space, unknown destination, and sparse signals over the KG, so informative and effective guidance is needed to achieve a satisfactory recommendation quality. To this end, we propose a CoArse-to-FinE neural symbolic reasoning approach (CAFE). It first generates user profiles as coarse sketches of user behaviors, which subsequently guide a path-finding process to derive reasoning paths for recommendations as fine-grained predictions. User profiles can capture prominent user behaviors from the history, and provide valuable signals about which kinds of path patterns are more likely to lead to potential items of interest for the user. To better exploit the user profiles, an improved path-finding algorithm called Profile-guided Path Reasoning (PPR) is also developed, which leverages an inventory of neural symbolic reasoning modules to effectively and efficiently find a batch of paths over a large-scale KG. We extensively experiment on four real-world benchmarks and observe substantial gains in the recommendation performance compared with state-of-the-art methods.

    Original languageEnglish (US)
    Title of host publicationCIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management
    PublisherAssociation for Computing Machinery
    Pages1645-1654
    Number of pages10
    ISBN (Electronic)9781450368599
    DOIs
    StatePublished - Oct 19 2020
    Event29th ACM International Conference on Information and Knowledge Management, CIKM 2020 - Virtual, Online, Ireland
    Duration: Oct 19 2020Oct 23 2020

    Publication series

    NameInternational Conference on Information and Knowledge Management, Proceedings

    Conference

    Conference29th ACM International Conference on Information and Knowledge Management, CIKM 2020
    CountryIreland
    CityVirtual, Online
    Period10/19/2010/23/20

    Keywords

    • explainable recommendation
    • knowledge graph
    • neural symbolic reasoning
    • path reasoning
    • recommender systems

    ASJC Scopus subject areas

    • Business, Management and Accounting(all)
    • Decision Sciences(all)

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