Privacy-Preserving Cross-Domain Recommendation with Federated Graph Learning

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Changxin Tian, Yuexiang Xie, Xu Chen, Yaliang Li, Wayne Xin Zhao
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Abstract

As people inevitably interact with items across multiple domains or various platforms, cross-domain recommendation (CDR) has gained increasing attention. However, the rising privacy concerns limit the practical applications of existing CDR models since they assume that full or partial data are accessible among different domains. Recent studies on privacy-aware CDR models neglect the heterogeneity from multiple domain data and fail to achieve consistent improvements in cross-domain recommendation; thus, it remains a challenging task to conduct effective CDR in a privacy-preserving way.

In this paper, we propose a novel federated graph learning approach for Privacy-Preserving Cross-Domain Recommendation (denoted as PPCDR) to capture users’ preferences based on distributed multi-domain data and improve recommendation performance for all domains without privacy leakage. The main idea of PPCDR is to model both global preference among multiple domains and local preference at a specific domain for a given user, which characterizes the user’s shared and domain-specific tastes towards the items for interaction. Specifically, in the private update process of PPCDR, we design a graph transfer module for each domain to fuse global and local user preferences and update them based on local domain data. In the federated update process, through applying the local differential privacy (LDP) technique for privacy-preserving, we collaboratively learn global user preferences based on multi-domain data, and adapt these global preferences to heterogeneous domain data via personalized aggregation. In this way, PPCDR can effectively approximate the multi-domain training process that directly shares local interaction data in a privacy-preserving way. Extensive experiments on three CDR datasets demonstrate that PPCDR consistently outperforms competitive single- and cross-domain baselines and effectively protects domain privacy.

利用联合图谱学习进行隐私保护跨域推荐
由于人们不可避免地要与多个领域或各种平台上的项目进行交互,跨领域推荐(CDR)越来越受到人们的关注。然而,由于现有的 CDR 模型假定不同域之间可以访问全部或部分数据,因此人们对隐私的日益关注限制了这些模型的实际应用。最近关于隐私感知 CDR 模型的研究忽视了来自多个域数据的异质性,无法实现跨域推荐的持续改进;因此,以保护隐私的方式进行有效的 CDR 仍然是一项具有挑战性的任务。在本文中,我们提出了一种用于隐私保护跨域推荐(Privacy-Preserving Cross-Domain Recommendation,简称 PPCDR)的新型联合图学习方法,以捕获基于分布式多域数据的用户偏好,并在不泄露隐私的情况下提高所有域的推荐性能。PPCDR 的主要思想是为给定用户建立多域之间的全域偏好和特定域的局部偏好模型,从而描述用户对交互项目的共享品味和特定域品味。具体来说,在 PPCDR 的私有更新过程中,我们为每个域设计了一个图转移模块,以融合全局和本地用户偏好,并根据本地域数据进行更新。在联合更新过程中,通过应用保护隐私的本地差异隐私(LDP)技术,我们基于多域数据协同学习全局用户偏好,并通过个性化聚合使这些全局偏好适应异构域数据。这样,PPCDR 就能以保护隐私的方式有效逼近直接共享本地交互数据的多域训练过程。在三个 CDR 数据集上进行的广泛实验表明,PPCDR 的性能始终优于具有竞争力的单域和跨域基线,并能有效保护域隐私。
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来源期刊
ACM Transactions on Information Systems
ACM Transactions on Information Systems 工程技术-计算机:信息系统
CiteScore
9.40
自引率
14.30%
发文量
165
审稿时长
>12 weeks
期刊介绍: The ACM Transactions on Information Systems (TOIS) publishes papers on information retrieval (such as search engines, recommender systems) that contain: new principled information retrieval models or algorithms with sound empirical validation; observational, experimental and/or theoretical studies yielding new insights into information retrieval or information seeking; accounts of applications of existing information retrieval techniques that shed light on the strengths and weaknesses of the techniques; formalization of new information retrieval or information seeking tasks and of methods for evaluating the performance on those tasks; development of content (text, image, speech, video, etc) analysis methods to support information retrieval and information seeking; development of computational models of user information preferences and interaction behaviors; creation and analysis of evaluation methodologies for information retrieval and information seeking; or surveys of existing work that propose a significant synthesis. The information retrieval scope of ACM Transactions on Information Systems (TOIS) appeals to industry practitioners for its wealth of creative ideas, and to academic researchers for its descriptions of their colleagues'' work.
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