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Reinforced KGs reasoning for explainable sequential recommendation

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Abstract

We explore the semantic-rich structured information derived from the knowledge graphs (KGs) associated with the user-item interactions and aim to reason out the motivations behind each successful purchase behavior. Existing works on KGs-based explainable recommendations focus purely on path reasoning based on current user-item interactions, which generally result in the incapability of conjecturing users’ subsequence preferences. Considering this, we attempt to model the KGs-based explainable recommendation in sequential settings. Specifically, we propose a novel architecture called Reinforced Sequential Learning with Gated Recurrent Unit (RSL-GRU), which is composed of a Reinforced Path Reasoning Network (RPRN) component and a GRU component. RSL-GRU takes users’ sequential behaviors and their associated KGs in chronological order as input and outputs potential top-N items for each user with appropriate reasoning paths from a global perspective. Our RPRN features a remarkable path reasoning capacity, which is regulated by a user-conditioned derivatively action pruning strategy, a soft reward strategy based on an improved multi-hop scoring function, and a policy-guided sequential path reasoning algorithm. Experimental results on four of Amazon’s large-scale datasets show that our method achieves excellent results compared with several state-of-the-art alternatives.

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  1. https://nijianmo.github.io/amazon/index.html

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Acknowledgements

The authors would like to acknowledge the support provided by the National Natural Science Foundation of China under Grant 61872222, the Natural Science Foundation of Shandong Province (ZR2020LZH011), the Young Scholars Program of Shandong University, and the ARC Discovery Project (Grant No. DP200101374, LP170100891, and DP190101985).

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Authors and Affiliations

  1. School of Software, Shandong University, Jinan, China

    Zhihong Cui, Lizhen Cui & Shijun Liu

  2. University of Technology Sydney, Ultimo, Australia

    Hongxu Chen & Guandong Xu

  3. School of Computer Science and Technology, Jilin University, Changchun, 130012, China

    Xueyan Liu

  4. The University of Queensland, Brisbane, Australia

    Hongzhi Yin

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  1. Zhihong Cui
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Correspondence to Hongxu Chen or Shijun Liu.

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This article belongs to the Topical Collection: Special Issue on Large Scale Graph Data Analytics

Guest Editors: Xuemin Lin, Lu Qin, Wenjie Zhang, and Ying Zhang

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Cui, Z., Chen, H., Cui, L. et al. Reinforced KGs reasoning for explainable sequential recommendation. World Wide Web 25, 631–654 (2022). https://doi.org/10.1007/s11280-021-00902-6

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