LacGCL: Lightweight message masking with linear attention and cross-view interaction graph contrastive learning for recommendation

IF 7.4 1区 管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Haohe Jia , Peng Hou , Yong Zhou , Hongbin Zhu , Hongfeng Chai
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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.
LacGCL:利用线性注意力和跨视图交互图对比学习进行轻量级信息屏蔽,以促进推荐
最近,图形对比学习(GCL)在推荐系统领域引起了极大关注。然而,许多 GCL 方法旨在通过采用密集矩阵运算和频繁操作图结构来生成对比视图,从而提高推荐准确性,这导致了大量的计算资源消耗。虽然较简单的 GCL 方法计算成本较低,但它们无法充分利用协同过滤信息,导致准确性降低。另一方面,更复杂的自适应方法可以获得更高的准确度,但却要以显著增加计算成本为代价。为了解决这个问题,并在保持低计算成本的同时实现高预测准确性,我们提出了一种新方法,该方法结合了带有信息屏蔽和跨视图交互的注意力图重构,用于对比学习。带有信息掩码的注意力导向图重构保留了图的结构和语义信息,同时缓解了过拟合问题。线性注意力确保了算法的低复杂度。此外,跨视图交互能够捕捉到更多高质量的潜在特征。我们在四个数据集上验证的结果表明,所提出的方法保持了较低的计算成本,并且在推荐准确性上明显优于基线方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Information Processing & Management
Information Processing & Management 工程技术-计算机:信息系统
CiteScore
17.00
自引率
11.60%
发文量
276
审稿时长
39 days
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.
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