{"title":"LacGCL: Lightweight message masking with linear attention and cross-view interaction graph contrastive learning for recommendation","authors":"Haohe Jia , Peng Hou , Yong Zhou , Hongbin Zhu , Hongfeng Chai","doi":"10.1016/j.ipm.2024.103930","DOIUrl":null,"url":null,"abstract":"<div><div>Graph contrastive learning (GCL) has recently attracted significant attention in the field of recommender systems. However, many GCL methods aim to enhance recommendation accuracy by employing dense matrix operations and frequent manipulation of graph structures to generate contrast views, leading to substantial computational resource consumption. While simpler GCL methods have lower computational costs, they fail to fully exploit collaborative filtering information, leading to reduced accuracy. On the other hand, more complex adaptive methods achieve higher accuracy but at the expense of significantly greater computational cost. Consequently, there exists a considerable gap in accuracy between these lightweight models and the more complex GCL methods focused on high accuracy.</div><div>To address this issue and achieve high predictive accuracy while maintaining low computational cost, we propose a novel method that incorporates attention-wise graph reconstruction with message masking and cross-view interaction for contrastive learning. The attention-wise graph reconstruction with message masking preserves the structural and semantic information of the graph while mitigating the overfitting problem. Linear attention ensures that the algorithm’s complexity remains low. Furthermore, the cross-view interaction is capable of capturing more high-quality latent features. Our results, validated on four datasets, demonstrate that the proposed method maintains a lightweight computational cost and significantly outperforms the baseline methods in recommendation accuracy.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 1","pages":"Article 103930"},"PeriodicalIF":7.4000,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Processing & Management","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0306457324002899","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Graph contrastive learning (GCL) has recently attracted significant attention in the field of recommender systems. However, many GCL methods aim to enhance recommendation accuracy by employing dense matrix operations and frequent manipulation of graph structures to generate contrast views, leading to substantial computational resource consumption. While simpler GCL methods have lower computational costs, they fail to fully exploit collaborative filtering information, leading to reduced accuracy. On the other hand, more complex adaptive methods achieve higher accuracy but at the expense of significantly greater computational cost. Consequently, there exists a considerable gap in accuracy between these lightweight models and the more complex GCL methods focused on high accuracy.
To address this issue and achieve high predictive accuracy while maintaining low computational cost, we propose a novel method that incorporates attention-wise graph reconstruction with message masking and cross-view interaction for contrastive learning. The attention-wise graph reconstruction with message masking preserves the structural and semantic information of the graph while mitigating the overfitting problem. Linear attention ensures that the algorithm’s complexity remains low. Furthermore, the cross-view interaction is capable of capturing more high-quality latent features. Our results, validated on four datasets, demonstrate that the proposed method maintains a lightweight computational cost and significantly outperforms the baseline methods in recommendation accuracy.
期刊介绍:
Information Processing and Management is dedicated to publishing cutting-edge original research at the convergence of computing and information science. Our scope encompasses theory, methods, and applications across various domains, including advertising, business, health, information science, information technology marketing, and social computing.
We aim to cater to the interests of both primary researchers and practitioners by offering an effective platform for the timely dissemination of advanced and topical issues in this interdisciplinary field. The journal places particular emphasis on original research articles, research survey articles, research method articles, and articles addressing critical applications of research. Join us in advancing knowledge and innovation at the intersection of computing and information science.