{"title":"SGNNRec: A Scalable Double-Layer Attention-Based Graph Neural Network Recommendation Model","authors":"Jing He, Le Tang, Dan Tang, Ping Wang, Li Cai","doi":"10.1007/s11063-024-11555-7","DOIUrl":null,"url":null,"abstract":"<p>Due to the information from the multi-relationship graphs is difficult to aggregate, the graph neural network recommendation model focuses on single-relational graphs (e.g., the user-item rating bipartite graph and user-user social relationship graphs). However, existing graph neural network recommendation models have insufficient flexibility. The recommendation accuracy instead decreases when low-quality auxiliary information is aggregated in the recommendation model. This paper proposes a scalable graph neural network recommendation model named SGNNRec. SGNNRec fuse a variety of auxiliary information (e.g., user social information, item tag information and user-item interaction information) beside user-item rating as supplements to solve the problem of data sparsity. A tag cluster-based item-semantic graph method and an apriori algorithm-based user-item interaction graph method are proposed to realize the construction of graph relations. Furthermore, a double-layer attention network is designed to learn the influence of latent factors. Thus, the latent factors are to be optimized to obtain the best recommendation results. Empirical results on real-world datasets verify the effectiveness of our model. SGNNRec can reduce the influence of poor auxiliary information; moreover, with increasing the number of auxiliary information, the model accuracy improves.</p>","PeriodicalId":51144,"journal":{"name":"Neural Processing Letters","volume":"25 1","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neural Processing Letters","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s11063-024-11555-7","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Due to the information from the multi-relationship graphs is difficult to aggregate, the graph neural network recommendation model focuses on single-relational graphs (e.g., the user-item rating bipartite graph and user-user social relationship graphs). However, existing graph neural network recommendation models have insufficient flexibility. The recommendation accuracy instead decreases when low-quality auxiliary information is aggregated in the recommendation model. This paper proposes a scalable graph neural network recommendation model named SGNNRec. SGNNRec fuse a variety of auxiliary information (e.g., user social information, item tag information and user-item interaction information) beside user-item rating as supplements to solve the problem of data sparsity. A tag cluster-based item-semantic graph method and an apriori algorithm-based user-item interaction graph method are proposed to realize the construction of graph relations. Furthermore, a double-layer attention network is designed to learn the influence of latent factors. Thus, the latent factors are to be optimized to obtain the best recommendation results. Empirical results on real-world datasets verify the effectiveness of our model. SGNNRec can reduce the influence of poor auxiliary information; moreover, with increasing the number of auxiliary information, the model accuracy improves.
期刊介绍:
Neural Processing Letters is an international journal publishing research results and innovative ideas on all aspects of artificial neural networks. Coverage includes theoretical developments, biological models, new formal modes, learning, applications, software and hardware developments, and prospective researches.
The journal promotes fast exchange of information in the community of neural network researchers and users. The resurgence of interest in the field of artificial neural networks since the beginning of the 1980s is coupled to tremendous research activity in specialized or multidisciplinary groups. Research, however, is not possible without good communication between people and the exchange of information, especially in a field covering such different areas; fast communication is also a key aspect, and this is the reason for Neural Processing Letters