异构信息网络的注意力感知多通道图卷积评分预测模型

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

异构信息网络(Heterogeneous Information Network,HIN)包含了丰富的语义信息,利用其进行评分预测已成为缓解推荐系统数据稀疏性问题的一个重要途径。然而,传统采用元路径来提取HIN语义信息的方法忽略了元路径中的评分信息,从而导致元路径无法精确捕获用户和推荐项目之间的语义相似性,同时也未能良好区分不同元路径的重要性。为了解决这两个问题,首先提出了一种带有评分限制的元路径以获取更准确的HIN语义信息,利用这些信息构建用户和项目多层网络;然后结合图卷积网络和注意力机制设计了一个用于评分预测的神经网络,通过多通道图卷积有效地表示了HIN的多种语义信息,采用注意力机制区分不同元路径的重要性,弥补了传统方法的不足;最后融合了用户和项目的属性信息,进一步提高了评分预测的准确性。在Douban Book和Yelp数据集上的实验结果表明所提模型明显优于对比的基线模型,尤其在数据稀疏的情况下,均方根误差比基线模型最多减少了50%,从而验证了所提模型的优越性。

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.