4.7 Article

Micro-behaviour with Reinforcement Knowledge-aware Reasoning for Explainable Recommendation

期刊

KNOWLEDGE-BASED SYSTEMS
卷 251, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.knosys.2022.109300

关键词

Micro -behaviour; Knowledge graph; Deep reinforcement learning; Recommendation; Explanation

资金

  1. Plan For Scientific Innovation Talent of He?nan Province, China [184100510012]
  2. Key Technologies R&D Program of He?nan Province, China [212102210084]
  3. Innovation Scientists and Technicians Troop Construction Projects of He?nan Province Key Technologies R & D Program of Henan Province, China [192102210295]

向作者/读者索取更多资源

Existing recommendation methods neglect the relationship between micro-behavior and knowledge graph and the explicit reasoning for user-item interaction data. This paper proposes a model that incorporates micro-behavior and knowledge graph into reinforcement learning for explainable recommendation, achieving better recommendation results.
Existing practical recommendation scenarios involve multiple micro-behaviour user-item interactions, such as clicks, page views, add-to-favourites, and purchases, which provide fine-grained and a better in-depth understanding of the user's preference. Furthermore, some recommendation methods have incorporated item knowledge into the micro-behaviour of user-item interaction. Although some have proved effective, two insights are often neglected. First, they fail to combine micro-behaviour with the relation of the knowledge graph (KG), and the semantic relationship between micro-behaviour and relation is not captured. Second, they do not provide explicit reasoning for micro-behaviour from user-item interaction data. These insights motivated us to propose a novel model of Micro-behaviour with Reinforcement Knowledge-aware Reasoning for Explainable Recommendation (MBKR), which incorporates micro-behaviour and the KG into reinforcement learning for explainable recommendation. Specifically, the model learns the behaviour by user-item propagation and the relation from the KG and combines the two to calculate the behavioural strength to mine user's interests. In addition, we designed a Shawo-relational path that combines recommendation and interpretability by providing rational paths; these paths capture the semantics of behaviours and relations. Finally, we extensively evaluated our method on several large-scale benchmark datasets, and the results indicate that the proposed method is more effective in providing recommendations than state-of-the-art methods.(c) 2022 Elsevier B.V. All rights reserved.

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