期刊
2022 IEEE 38TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2022)
卷 -, 期 -, 页码 299-311出版社
IEEE COMPUTER SOC
DOI: 10.1109/ICDE53745.2022.00027
关键词
Knowledge-aware Recommendation; Knowledge Graphs; Graph Convolutional Networks; Collaborative Guidance
类别
资金
- Research Grants Council of Hong Kong [CUHK 2300174, C5026-18GF, CUHK 3133238]
This paper proposes a novel knowledge-aware recommendation model CG-KGR, which can effectively learn knowledge graphs and user-item interactions, resulting in more precise personalized recommendations.
To alleviate data sparsity and cold-start problems of traditional recommender systems (RSs), incorporating knowledge graphs (KGs) to supplement auxiliary information has attracted considerable attention recently. However, simply integrating KGs in current KG-based RS models is not necessarily a guarantee to improve the recommendation performance, which may even weaken the holistic model capability. This is because the construction of these KGs is independent of the collection of historical user-item interactions; hence, information in these KGs may not always be helpful for recommendation to all users. In this paper, we propose attentive Knowledge-aware Graph convolutional networks with Collaborative Guidance for personalized Recommendation (CG-KGR). CG-KGR is a novel knowledge aware recommendation model that enables ample and coherent learning of KGs and user-item interactions, via our proposed Collaborative Guidance Mechanism. Specifically, CG-KGR first encapsulates historical interactions to interactive information summarization. Then CG-KGR utilizes it as guidance to extract information out of KGs, which eventually provides more precise personalized recommendation. We conduct extensive experiments on four real-world datasets over two recommendation tasks, i.e., Top-K recommendation and Click-Through rate (CTR) prediction. The experimental results show that the CG-KGR model significantly outperforms recent state-of-the-art models by 1.4-27.0% in terms of Recall metric on Top-K recommendation.
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