融合画像和文本信息的轻量级关系图注意推荐模型  

Light relational graph attention recommendation model fusing with profiles and text information

作　　者：刘超[1] 朱波 Liu Chao;Zhu Bo(College of Computer Science&Engineering,Chongqing University of Technology,Chongqing 400054,China)

机构地区：[1]重庆理工大学计算机科学与工程学院,重庆400054

出　　处：《计算机应用研究》2023年第4期1037-1043,共7页Application Research of Computers

基　　金：国家教育考试科研规划课题(GJK2019006)。

摘　　要：针对当前基于图神经网络的推荐系统受数据稀疏影响推荐效率不高的问题,提出融合画像和文本信息的轻量级关系图注意推荐模型(LightRGAN)。首先,利用用户画像和项目画像初始化用户和项目的嵌入表示。其次,引入评论、项目描述和项目类型作为辅助信息,并通过基于多头注意力机制的文本嵌入网络挖掘同一用户评论集和描述集中文本之间的潜在联系。然后,通过融合注意力机制的轻量级关系图卷积网络学习用户和项目的嵌入表示。最后,对各层嵌入表示加权求和并通过预测网络计算匹配分数。在三个公开数据集上的实验结果表明LightRGAN的效果优于多个现有的基线模型,评估指标HR@20、NDCG@20较最优基线模型最少提升了2.58%、2.37%。To address the problem of poor performance in graph neural network based recommendation systems due to the data sparse problem,this paper proposed a light relational graph attention recommendation model(LightRGAN)that fusing with profiles and text information.First,this model used user profiles and item profiles to initialize the embeddings of users and the items.Secondly,it introduced comments,descriptions and categories as auxiliary information.Meanwhile,it mined the relationships between the text in the comment set and the description set of the same user through the text embedding network based on the multi-head attention mechanism.Then,it learned the embedding representations of the users and the items through a light relational graph convolutional network fused with the attention mechanism.Finally,it weighted sum the embedding representations at each layer and inputted them to the prediction network to calculated match score between the users and the items.The results on three public datasets show that the LightRGAN outperforms other baseline models.The evaluation metrics HR@20 and NDCG@20 improved at least 2.58%and 2.37%than the optimal baseline model.

关 键 词：推荐系统 图神经网络 用户画像 文本信息 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]