Attentive Knowledge-aware Graph Convolutional Networks with Collaborative Guidance for Recommendation

• URL: http://arxiv.org/abs/2109.02046v1
• Date: Sun, 5 Sep 2021 11:55:20 GMT
• Title: Attentive Knowledge-aware Graph Convolutional Networks with Collaborative Guidance for Recommendation
• Authors: Yankai Chen, Yaming Yang, Yujing Wang, Jing Bai, Xiangchen Song, Irwin King
• Abstract summary: We propose attentive Knowledge-aware 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. We conduct extensive experiments on four real-world datasets over two recommendation tasks.
• Score: 36.95691423601792
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 4.0-53.2% and 0.4-3.2%, in terms of Recall metric on Top-K recommendation and AUC on CTR prediction, respectively.

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