为推荐而进行的图增强对比学习

IF 8.3 2区 材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY
Lixiang Xu, Yusheng Liu, Tong Xu, Enhong Chen, Y. Tang
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引用次数: 0

摘要

将对比学习(CL)应用于推荐系统中的协同过滤(CF)已取得显著成效。基于对比学习的推荐模型主要侧重于通过采用不同的图增强方法创建多个增强视图,并利用这些视图进行自监督学习。然而,目前用于推荐系统的 CL 方法通常很难完全解决噪声数据问题。为了解决这个问题,我们提出了用于推荐框架的图增强增强增强对比(GAECL),该框架使用基于拓扑和语义双重适应的图增强,并通过结构优化进行全局协同建模,以共同创建对比视图,从而更好地增强 CF 范例。具体来说,我们通过重构邻接矩阵严格过滤掉不重要的拓扑结构,并根据 PageRank 中心性原则屏蔽节点中不重要的属性,从而生成可过滤掉噪声数据的增强视图。此外,GAECL 还通过结构优化实现全局协作建模,并根据 PageRank 中心性原则生成另一个增强视图。这有助于过滤噪声数据,同时保留数据的原始语义,实现更有效的数据增强。我们在五个数据集上进行了广泛的实验,证明我们的模型比各种推荐模型性能更优越。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Graph Augmentation Empowered Contrastive Learning for Recommendation
The application of contrastive learning (CL) to collaborative filtering (CF) in recommender systems has achieved remarkable success. CL-based recommendation models mainly focus on creating multiple augmented views by employing different graph augmentation methods and utilizing these views for self-supervised learning. However, current CL methods for recommender systems usually struggle to fully address the problem of noisy data. To address this problem, we propose the G raph A ugmentation E mpowered C ontrastive L earning (GAECL) for recommendation framework, which uses graph augmentation based on topological and semantic dual adaptation and global co-modeling via structural optimization to co-create contrasting views for better augmentation of the CF paradigm. Specifically, we strictly filter out unimportant topologies by reconstructing the adjacency matrix and mask unimportant attributes in nodes according to the PageRank centrality principle to generate an augmented view that filters out noisy data. Additionally, GAECL achieves global collaborative modeling through structural optimization and generates another augmented view based on the PageRank centrality principle. This helps to filter the noisy data while preserving the original semantics of the data for more effective data augmentation. Extensive experiments are conducted on five datasets to demonstrate the superior performance of our model over various recommendation models.
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来源期刊
ACS Applied Materials & Interfaces
ACS Applied Materials & Interfaces 工程技术-材料科学:综合
CiteScore
16.00
自引率
6.30%
发文量
4978
审稿时长
1.8 months
期刊介绍: ACS Applied Materials & Interfaces is a leading interdisciplinary journal that brings together chemists, engineers, physicists, and biologists to explore the development and utilization of newly-discovered materials and interfacial processes for specific applications. Our journal has experienced remarkable growth since its establishment in 2009, both in terms of the number of articles published and the impact of the research showcased. We are proud to foster a truly global community, with the majority of published articles originating from outside the United States, reflecting the rapid growth of applied research worldwide.
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