Temporal meta-path guided explainable recommendation

Hongxu CHEN, Yicong LI, Xiangguo SUN, Guandong XU, Hongzhi YIN

Research output: Chapter in Book/Report/Conference proceedingChapters

60 Citations (Scopus)

Abstract

Recent advances in path-based explainable recommendation systems have attracted increasing attention thanks to the rich information provided by knowledge graphs. Most existing explainable recommendation only utilizes static knowledge graph and ignores the dynamic user-item evolutions, leading to less convincing and inaccurate explanations. Although there are some works that realize that modelling user's temporal sequential behaviour could boost the performance and explainability of the recommender systems, most of them either only focus on modelling user's sequential interactions within a path or independently and separately of the recommendation mechanism. In this paper, we propose a novel Temporal Meta-path Guided Explainable Recommendation (TMER), which utilizes well-designed item-item path modelling between consecutive items with attention mechanisms to sequentially model dynamic user-item evolutions on dynamic knowledge graph for explainable recommendations. Compared with existing works that use heavy recurrent neural networks to model temporal information, we propose simple but effective neural networks to capture users' historical item features and path-based context to characterise next purchased item. Extensive evaluations of TMER on three real-world benchmark datasets show state-of-the-art performance compared against recent strong baselines. Copyright © 2021 Association for Computing Machinery.

Original languageEnglish
Title of host publicationProceedings of the 14th ACM International Conference on Web Search and Data Mining
Place of PublicationNew York
PublisherThe Association for Computing Machinery
Pages1056-1064
ISBN (Electronic)9781450382977
DOIs
Publication statusPublished - 2021

Citation

Chen, H., Li, Y., Sun, X., Xu, G., & Yin, H. (2021). Temporal meta-path guided explainable recommendation. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (pp. 1056-1064). The Association for Computing Machinery. https://doi.org/10.1145/3437963.3441762

Keywords

  • Explainable recommendation
  • Temporal recommendation

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