KEMIM: knowledge-enhanced user multi-interest modeling for recommender systems
MetadataShow full item record
AbstractResearchers 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.
CitationYang F, Yue Y, Li G, Payne TR, Man KL (2023) 'KEMIM: knowledge-enhanced user multi-interest modeling for recommender systems', IEEE Access, 11, pp.55425-55434.
SponsorsThis work was supported in part by the Basic Public Welfare Research Project of Zhejiang, China, under Grant LGF20G020001; in part by the AI University Research Centre (AI-URC) through the Xi’an Jiaotong-Liverpool University Key Program Special Fund under Grant KSF-A-17; and in part by the Suzhou Municipal Key Laboratory for Intelligent Virtual Engineering under Grant SZS2022004.
The following license files are associated with this item:
- Creative Commons
Except where otherwise noted, this item's license is described as Green - can archive pre-print and post-print or publisher's version/PDF