Reinforced KGs reasoning for explainable sequential recommendation

Zhihong CUI, Hongxu CHEN, Lizhen CUI, Shijun LIU, Xueyan LIU, Guandong XU, Hongzhi YIN

Research output: Contribution to journalArticlespeer-review

14 Citations (Scopus)

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. Copyright © 2021 The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Original languageEnglish
Pages (from-to)631-654
JournalWorld Wide Web
Volume25
Early online dateJun 2021
DOIs
Publication statusPublished - Mar 2022

Citation

Cui, Z., Chen, H., Cui, L., Liu, S., Liu, X., Xu, G., & Yin, H. (2022). Reinforced KGs reasoning for explainable sequential recommendation. World Wide Web, 25, 631-654. https://doi.org/10.1007/s11280-021-00902-6

Keywords

  • Reinforcement learning
  • Sequential recommendation
  • Path reasoning

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