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KEMIM: knowledge-enhanced user multi-interest modeling for recommender systems

  • Fan Yang
    ,
  • Yong Yue
    ,
  • Gangmin Li
    ,
  • Terry R. Payne
    ,
  • Ka Lok Man
  • Xi'an Jiaotong-Liverpool University
    ,
  • University of Liverpool
Research Output: Contribution to journal Article Peer-review

Open access

Abstract

Researchers typically leverage side information, such as social networks or the knowledge graph, to overcome the sparsity and cold start problem in collaborative filtering. To tackle the limitations of existing user interest modeling, we propose a knowledge-enhanced user multi-interest modeling for recommender systems (KEMIM). First, we utilize the user-item historical interaction as the knowledge graph’s head entity to create a user’s explicit interests and leverage the relationship path to expand the user’s potential interests through connections in the knowledge graph. Second, considering the diversity of a user’s interests, we adopt an attention mechanism to learn the user’s attention to each historical interaction and each potential interest. Third, we combine the user’s attribute features with interests to solve the cold start problem effectively. With the knowledge graph’s structural data, KEMIM could describe the features of users at a fine granularity and provide explainable recommendation results to users. In this study, we conduct an in-depth empirical evaluation across three open datasets for two different recommendation tasks: Click-Through rate (CTR) prediction and Top-K recommendation. The experimental findings demonstrate that KEMIM outperforms several state-of-the-art baselines.

Publication Information

Output type

Research Output: Contribution to journal Article Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 55425-55434 (10 pages)

Journal (Volume, Issue Number)

IEEE Access (Volume 11)

Publication milestones

  • Accepted/In press - 31/03/2023
  • Published - 05/04/2023

Publication status

Published - 05/04/2023

External Publication IDs

  • handle.net: 10547/625919
  • Scopus: 85153384772

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