Knowledge-Aware Graph Collaborative Filtering for Recommender Systems

Abstract

To solve the data sparseness and cold start problems in collaborative filtering (CF) based recommender systems (RS), various complex algorithms are proposed to extract and integrate explicit or implicit information of data for the recommendation. In this paper, we propose to aggregate and transmit the rich semantic information with the help of knowledge graph (KG) that is regarded as one of the main sources of auxiliary information. Specifically, we first propose a Neural Graph Collaborative Filtering to construct and aggregate information. And then we build a scalable and end-to-end knowledge-aware graph collaborative filtering model named KGCF. In KGCF, neighbourhood information in KG is encoded to construct information in a complex new way. And the information from neighbours are merged with a personalized bias calculated by attention mechanism based on KG. In order to extend the interacted items and capture the high-level semantic information of KG, multiple KGCF layers stacked is used in KGCF. Experimental results on three real data sets indicate that the KGCF model proposed in this paper is superior to the existing models in terms of accuracy and can also effectively solve the data sparsity problem of RS.