Journal
KNOWLEDGE-BASED SYSTEMS
Volume 257, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.knosys.2022.109903
Keywords
Tag-aware recommendation; Recommendation systems; Knowledge graph; Social network
Categories
Funding
- National Natural Science Foundation of China [61906037, 61972085]
- Natural Science Foundation of Jiangsu Province [BK20190335, BK20190345]
- Fundamental Research Funds for the Central Universities
- Jiangsu Provincial Key Laboratory of Network and Information Security [BM2003201]
- Key Laboratory of Computer Network and Information Integration of Ministry of Education of China [93K-9]
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This paper proposes a Knowledge-enhanced Tag-aware Recommendation System (KTRS) that incorporates auxiliary knowledge to improve the performance of tag-aware recommendation systems. Experimental results demonstrate that the proposed system outperforms other recommendation methods on real-world datasets, highlighting the effectiveness of auxiliary knowledge.
In recent practices, sparsity problems often arise in recommendation systems, resulting in weak generalization ability. To alleviate this problem, tag-aware recommendation systems (TRS) leverage personalized tags to enhance the modeling of user preferences and item characteristics. However, current tag-aware methods suffer from arbitrary user behaviors as they limit the additional information only to user tags. In this paper, we investigate a more general scenario, namely Knowledge-enhanced Tag-aware Recommendation System (KTRS) which involves auxiliary knowledge compared with TRS. Correspondingly, we propose a novel recommendation model for KTRS, called TKGAT. It firstly constructs a collaborative recommendation graph and then learns heterogeneous representation via an multi-layer multi-head attention mechanism. Experiments conducted on real-world datasets demonstrate that the proposed system outperforms the state-of-the-art recommendation methods, and show effectiveness of the auxiliary knowledge. (c) 2022 Elsevier B.V. All rights reserved.
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