Attention-aware Multi-channel Graph Convolutional Rating Prediction Model for Heterogeneous Information Networks

Abstract

Heterogeneous information network(HIN) contains rich semantic information,and the use of HIN for rating prediction has become an important way to alleviate the problem of data sparsity in recommender systems.However,the traditional methods using meta-paths to extract HIN semantic information ignore the rating information on the edges,making the meta-paths unable to accurately capture the semantic similarity between users and recommended items.And these methods also fail to distinguish the importance of different meta-paths.To address the two problems,rating constrained meta-path is proposed to obtain more accurate HIN semantic information which is then used to construct multi-layer homogeneous networks for users and items.Then,a neural network for rating prediction is designed by combining graph convolutional network and attention mechanism,which effectively represents various semantic information in HIN through multi-channel graph convolutional networks and distinguishes the importance of different meta-paths by using an attentional fusion function.Furthermore,the proposed model also integrates the attribute information of users and items to improve the accuracy of rating prediction.Experimental results on Douban Book and Yelp datasets show that the proposed model is significantly better than the comparative baseline models,especially in the case of sparse data,and the root mean square error reduces by up to 50% compared to the baseline model,thus verifying the superiority of the proposed model.

Keywords

• rating prediction|meta-path|heterogeneous information network|attention mechanism|graph convolutional network