Knowledge Graph Embedding Based Collaborative Filtering
Yuhang Zhang, Jun Wang, Jie Luo
Abstract
Along with the rapidly increasing massive online data, recommender systems have been used as an effective approach for filtering useful information, which have been widely adopted in many web applications. In recent years, new techniques such as neural network and deep learning have demonstrated its effectiveness in studies of recommender systems. In this paper, we propose a novel approach for collaborative filtering with implicit feedback based on knowledge graph embedding. The basic ideal is to model the interactions between users and items as an interaction knowledge graph with a single relation, whose vector representation is learned through knowledge graph embedding. Base on the learned representation, the collaborative filtering problem is converted into link prediction in the interaction knowledge graph. KGECF, a neural network for knowledge graph embedding based collaborative filtering, is proposed based on this ideal and the RotatE knowledge graph embedding model. Experimental results on five datasets with different characteristics show that the KGECF model achieves the state-of-the-art (SOTA) performance on all datasets. And unlike other SOTA models that have obvious performance drops on AMusic and AToy datasets, our model's performance is very stable across all datasets.