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

TL;DR

This paper introduces CG-KGR, a novel knowledge-aware graph convolutional network that uses collaborative guidance to improve personalized recommendations by better integrating user interactions and knowledge graphs.

Contribution

The paper proposes a collaborative guidance mechanism that enhances the integration of knowledge graphs with user-item interactions for more effective personalized recommendations.

Findings

CG-KGR significantly outperforms state-of-the-art models in recall by 1.4-27.0%.

The model improves recommendation accuracy on four real-world datasets.

Extensive experiments validate the effectiveness of the proposed approach.

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 1.4-27.0% in terms of Recall metric on Top-K recommendation.