使用增强超图神经网络的联合社交推荐方法

IF 7.2 4区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Hongliang Sun, Zhiying Tu, Dianbo Sui, Bolin Zhang, Xiaofei Xu
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引用次数: 0

摘要

近年来,随着在线社交网络平台的发展,社交推荐系统的研究工作日益增多。与传统的推荐系统不同,社交推荐系统既利用用户与项目之间的互动,也利用用户与用户之间的社交关系来推荐相关项目,同时考虑到社交同质性和社交影响力。基于图神经网络(GNN)的社交推荐方法已被提出,以有效地模拟这些项目交互和社交关系。然而,现有的基于图神经网络的方法依赖于集中式训练,这会引发隐私问题,并且由于法规和隐私限制,在数据收集方面面临挑战。联盟学习作为一种保护隐私的替代方法应运而生。将联合学习与基于 GNN 的社交推荐方法相结合,可以发挥各自的优势,但也带来了新的挑战:1)现有的联合推荐系统往往缺乏处理异构数据的能力,如用户-项目交互和社会关系;2)由于分布在不同客户端的数据稀少,捕捉用户之间的高阶关系信息变得具有挑战性,而且往往被大多数联合推荐系统所忽视。为了克服这些挑战,我们提出了一种使用增强超图神经网络的联合社交推荐方法。我们引入超图神经网络(HGNN)来学习联合推荐系统中的用户和项目嵌入,利用超图结构来解决数据的异质性问题。基于精心制作的三角形图案,我们合并用户和项目节点,在本地客户端上构建超图,捕捉特定的三角形关系。我们使用多个 HGNN 通道来编码不同类别的高阶关系,并采用注意力机制来聚合这些通道中的嵌入信息。我们在真实世界社交推荐数据集上的实验证明了所提方法的有效性。在三个公开数据集上的广泛实验结果验证了所提方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Federated Social Recommendation Approach with Enhanced Hypergraph Neural Network

In recent years, the development of online social network platforms has led to increased research efforts in social recommendation systems. Unlike traditional recommendation systems, social recommendation systems utilize both user-item interactions and user-user social relations to recommend relevant items, taking into account social homophily and social influence. Graph neural network (GNN) based social recommendation methods have been proposed to model these item interactions and social relations effectively. However, existing GNN-based methods rely on centralized training, which raises privacy concerns and faces challenges in data collection due to regulations and privacy restrictions. Federated learning has emerged as a privacy-preserving alternative. Combining federated learning with GNN-based methods for social recommendation can leverage their respective advantages, but it also introduces new challenges: 1) existing federated recommendation systems often lack the capability to process heterogeneous data, such as user-item interactions and social relations; 2) due to the sparsity of data distributed across different clients, capturing the higher-order relationship information among users becomes challenging and is often overlooked by most federated recommendation systems. To overcome these challenges, we propose a federated social recommendation approach with enhanced hypergraph neural network. We introduce hypergraph graph neural networks (HGNN) to learn user and item embeddings in federated recommendation systems, leveraging the hypergraph structure to address the heterogeneity of data. Based on carefully crafted triangular motifs, we merge user and item nodes to construct hypergraphs on local clients, capturing specific triangular relations. Multiple HGNN channels are used to encode different categories of high-order relations, and an attention mechanism is applied to aggregate the embedded information from these channels. Our experiments on real-world social recommendation datasets demonstrate the effectiveness of the proposed approach. Extensive experiment results on three publicly available datasets validate the effectiveness of the proposed method.

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来源期刊
ACM Transactions on Intelligent Systems and Technology
ACM Transactions on Intelligent Systems and Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.30
自引率
2.00%
发文量
131
期刊介绍: ACM Transactions on Intelligent Systems and Technology is a scholarly journal that publishes the highest quality papers on intelligent systems, applicable algorithms and technology with a multi-disciplinary perspective. An intelligent system is one that uses artificial intelligence (AI) techniques to offer important services (e.g., as a component of a larger system) to allow integrated systems to perceive, reason, learn, and act intelligently in the real world. ACM TIST is published quarterly (six issues a year). Each issue has 8-11 regular papers, with around 20 published journal pages or 10,000 words per paper. Additional references, proofs, graphs or detailed experiment results can be submitted as a separate appendix, while excessively lengthy papers will be rejected automatically. Authors can include online-only appendices for additional content of their published papers and are encouraged to share their code and/or data with other readers.
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