KCRec: Knowledge-aware representation Graph Convolutional Network for Recommendation

Collaborative filtering (CF) usually suffers from data sparsity and cold-start problems in real recommendation scenarios, therefore, side information like social networks and contexts have been introduced to improve its performance. In this paper, we consider the knowledge graph (KG) as a source of side information and propose a novel framework, Knowledge-aware representation Graph Convolutional Network for Recommendation (KCRec), that is an end-to-end framework that captures the inter-user and inter-item relatedness effectively. For exploring the potential long-distance interests of the user, we aggregate the item features and get the representation of the user preferences by propagating the relationships in KG between their neighborhood, and further integrates with the graph convolution network. Furthermore, we employ similarity features in different users to construct a user-adjacency graph, and utilize the user-item interaction features to establish a user-feature graph, to obtain the high-order representation of users. Extensive experiments on two real-world datasets demonstrate that our proposed method has substantially improved, which outperforms several state-of-the-art baselines. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes a novel Knowledge-aware representation Graph Convolutional Network for Recommendation (KCRec) framework, which effectively addresses the data sparsity and cold-start problems in collaborative filtering in real recommendation scenarios. By utilizing knowledge graph and user-item relationships, the performance of the recommendation system is improved. Experimental results demonstrate that the proposed method outperforms several state-of-the-art baselines.