KEMIM: Knowledge-Enhanced User Multi-Interest Modeling for Recommender Systems

Yang, Fan, Yue, Yong, Li, Gangmin, Payne, Terry R ORCID: 0000-0002-0106-8731 and Man, Ka Lok (2023) KEMIM: Knowledge-Enhanced User Multi-Interest Modeling for Recommender Systems. IEEE ACCESS, 11. pp. 55425-55434.

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Official URL: http://dx.doi.org/10.1109/access.2023.3264550

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.

Item Type:	Article
Uncontrolled Keywords:	Knowledge graphs, Recommender systems, Feature extraction, Predictive models, Semantics, Collaborative filtering, Behavioral sciences, Multi-interest, user modeling, knowledge graph, recommender systems
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	06 Apr 2023 15:18
Last Modified:	15 Mar 2024 04:09
DOI:	10.1109/ACCESS.2023.3264550
Open Access URL:	https://ieeexplore.ieee.org/document/10092818
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3169483