{"title":"Privacy-Preserving Cross-Domain Recommendation with Federated Graph Learning","authors":"Changxin Tian, Yuexiang Xie, Xu Chen, Yaliang Li, Wayne Xin Zhao","doi":"10.1145/3653448","DOIUrl":null,"url":null,"abstract":"<p>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. </p><p>In this paper, we propose a novel federated graph learning approach for <b>P</b>rivacy-<b>P</b>reserving <b>C</b>ross-<b>D</b>omain <b>R</b>ecommendation (denoted as <b>PPCDR</b>) 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.</p>","PeriodicalId":50936,"journal":{"name":"ACM Transactions on Information Systems","volume":null,"pages":null},"PeriodicalIF":5.4000,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Information Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3653448","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
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.
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.