{"title":"FedGR: Cross-platform federated group recommendation system with hypergraph neural networks","authors":"Junlong Zeng, Zhenhua Huang, Zhengyang Wu, Zonggan Chen, Yunwen Chen","doi":"10.1007/s10844-024-00887-4","DOIUrl":null,"url":null,"abstract":"<p>Group recommendation systems are widely applied in social media, e-commerce, and diverse platforms. These systems face challenges associated with data privacy constraints and protection regulations, impeding the sharing of user data for model improvement. To address the issue of data silos, federated learning emerges as a viable solution. However, difficulties arise due to the non-independent and non-identically distributed nature of data across different platforms, affecting performance. Furthermore, conventional federated learning often overlooks individual differences among stakeholders. In response to these challenges, we propose a pioneering cross-platform federated group recommendation system named FedGR. FedGR integrates hypergraph convolution, attention aggregation, and fully connected fusion components with federated learning to ensure exceptional model performance while preserving the confidentiality of private data. Additionally, we introduce a novel federated model aggregation strategy that prioritizes models with high training effectiveness, thereby improving overall model performance. To address individual differences, we design a temporal personalization update strategy for updating item representations, allowing local models to focus more on their individual characteristics. To evaluate FedGR, we apply our approach to three real-world datasets, demonstrating the robust capabilities of our cross-platform group recommendation system.</p>","PeriodicalId":56119,"journal":{"name":"Journal of Intelligent Information Systems","volume":"17 1","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Intelligent Information Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s10844-024-00887-4","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Group recommendation systems are widely applied in social media, e-commerce, and diverse platforms. These systems face challenges associated with data privacy constraints and protection regulations, impeding the sharing of user data for model improvement. To address the issue of data silos, federated learning emerges as a viable solution. However, difficulties arise due to the non-independent and non-identically distributed nature of data across different platforms, affecting performance. Furthermore, conventional federated learning often overlooks individual differences among stakeholders. In response to these challenges, we propose a pioneering cross-platform federated group recommendation system named FedGR. FedGR integrates hypergraph convolution, attention aggregation, and fully connected fusion components with federated learning to ensure exceptional model performance while preserving the confidentiality of private data. Additionally, we introduce a novel federated model aggregation strategy that prioritizes models with high training effectiveness, thereby improving overall model performance. To address individual differences, we design a temporal personalization update strategy for updating item representations, allowing local models to focus more on their individual characteristics. To evaluate FedGR, we apply our approach to three real-world datasets, demonstrating the robust capabilities of our cross-platform group recommendation system.
期刊介绍:
The mission of the Journal of Intelligent Information Systems: Integrating Artifical Intelligence and Database Technologies is to foster and present research and development results focused on the integration of artificial intelligence and database technologies to create next generation information systems - Intelligent Information Systems.
These new information systems embody knowledge that allows them to exhibit intelligent behavior, cooperate with users and other systems in problem solving, discovery, access, retrieval and manipulation of a wide variety of multimedia data and knowledge, and reason under uncertainty. Increasingly, knowledge-directed inference processes are being used to:
discover knowledge from large data collections,
provide cooperative support to users in complex query formulation and refinement,
access, retrieve, store and manage large collections of multimedia data and knowledge,
integrate information from multiple heterogeneous data and knowledge sources, and
reason about information under uncertain conditions.
Multimedia and hypermedia information systems now operate on a global scale over the Internet, and new tools and techniques are needed to manage these dynamic and evolving information spaces.
The Journal of Intelligent Information Systems provides a forum wherein academics, researchers and practitioners may publish high-quality, original and state-of-the-art papers describing theoretical aspects, systems architectures, analysis and design tools and techniques, and implementation experiences in intelligent information systems. The categories of papers published by JIIS include: research papers, invited papters, meetings, workshop and conference annoucements and reports, survey and tutorial articles, and book reviews. Short articles describing open problems or their solutions are also welcome.