Multi-view graph contrastive representation learning for bundle recommendation

IF 7.4 1区 管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Peng Zhang , Zhendong Niu , Ru Ma , Fuzhi Zhang
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引用次数: 0

Abstract

Bundle recommendation can recommend a collection of associated items that can be consumed together to a user rather than recommending these items separately, making it extremely suitable for some scenarios such as product bundle recommendation and game bundle recommendation. Recent bundle recommendation approaches consider auxiliary data to mitigate sparse user-bundle interactions. However, these approaches obtain the node embeddings directly from the established user-bundle graph and do not explicitly exploit the relationships between users (bundles) when constructing recommendation models. Moreover, bundle recommendation approaches based on graph contrastive learning usually construct contrastive views by randomly discarding nodes (edges) in the graph, while discarding some essential nodes or edges will destroy the structure of the original graph, thereby deteriorating the quality of the learned node embeddings. Aiming at these limitations, we propose a bundle recommendation approach based on multi-view graph contrastive representation learning. First, we present a multi-view modeling method to model the relations between entities as several views from different perspectives. These views serve as inputs of graph neural networks for graph representation learning and provide contrastive views for the contrastive learning tasks. Second, we propose a novel framework for bundle recommendation. This framework obtains the user (bundle) embeddings from different views by performing multi-view graph representation learning and enhances the learned user and bundle embeddings through a two-level contrastive learning strategy. On this basis, the enhanced user (bundle) embeddings are fused for prediction. Finally, we design a joint optimization objective to optimize the model parameters, combining the prediction loss that supports multiple negative samples and the contrastive losses. Experiments on the Netease and Youshu datasets reveal that our approach outperforms the state-of-the-art (SOTA) baselines. Furthermore, the average improvements of Recall@K and NDCG@K of our approach over the SOTA baselines are approximately 3.38% and 2.80% on Netease and 3.94% and 4.84% on Youshu.
用于捆绑推荐的多视图对比表示学习
捆绑推荐可以向用户推荐一系列可以一起消费的关联项目,而不是单独推荐这些项目,因此非常适合一些场景,如产品捆绑推荐和游戏捆绑推荐。最近的捆绑推荐方法考虑了辅助数据,以减轻用户-捆绑交互的稀疏性。然而,这些方法直接从已建立的用户-捆绑图中获取节点嵌入,在构建推荐模型时没有明确利用用户(捆绑)之间的关系。此外,基于图对比学习的捆绑推荐方法通常通过随机丢弃图中的节点(边)来构建对比视图,而丢弃一些重要的节点或边会破坏原始图的结构,从而降低学习到的节点嵌入的质量。针对这些局限性,我们提出了一种基于多视图对比表示学习的捆绑推荐方法。首先,我们提出了一种多视图建模方法,将实体之间的关系建模为来自不同视角的多个视图。这些视图作为图神经网络的输入,用于图表示学习,并为对比学习任务提供对比视图。其次,我们提出了一种新颖的捆绑推荐框架。该框架通过执行多视角图表示学习从不同视角获取用户(捆绑)嵌入,并通过两级对比学习策略增强学习到的用户和捆绑嵌入。在此基础上,融合增强的用户(包)嵌入进行预测。最后,我们设计了一个联合优化目标,结合支持多个负样本的预测损失和对比损失来优化模型参数。在网易和优酷数据集上的实验表明,我们的方法优于最先进的(SOTA)基线。此外,与 SOTA 基线相比,我们的方法在网易数据集上的 Recall@K 和 NDCG@K 平均提高了约 3.38% 和 2.80%,在优树数据集上的 Recall@K 和 NDCG@K 平均提高了约 3.94% 和 4.84%。
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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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