{"title":"Handling Low Homophily in Recommender Systems With Partitioned Graph Transformer","authors":"Thanh Tam Nguyen;Thanh Toan Nguyen;Matthias Weidlich;Jun Jo;Quoc Viet Hung Nguyen;Hongzhi Yin;Alan Wee-Chung Liew","doi":"10.1109/TKDE.2024.3485880","DOIUrl":null,"url":null,"abstract":"Modern recommender systems derive predictions from an interaction graph that links users and items. To this end, many of today's state-of-the-art systems use graph neural networks (GNNs) to learn effective representations of these graphs under the assumption of homophily, i.e., the idea that similar users will sit close to each other in the graph. However, recent studies have revealed that real-world recommendation graphs are often heterophilous, i.e., dissimilar users will also often sit close to each other. One of the reasons for this heterophilia is shilling attacks that obscure the inherent characteristics of the graph and make the derived recommendations less accurate as a consequence. Hence, to cope with low homophily in recommender systems, we propose a recommendation model called PGT4Rec that is based on a Partitioned Graph Transformer. The model integrates label information into the learning process, which allows discriminative neighbourhoods of users to be generated. As such, the framework can both detect shilling attacks and predict user ratings for items. Extensive experiments on real and synthetic datasets show PGT4Rec as not only providing superior performance in these two tasks but also significant robustness to a range of adversarial conditions.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 1","pages":"334-350"},"PeriodicalIF":8.9000,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Knowledge and Data Engineering","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10737032/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Modern recommender systems derive predictions from an interaction graph that links users and items. To this end, many of today's state-of-the-art systems use graph neural networks (GNNs) to learn effective representations of these graphs under the assumption of homophily, i.e., the idea that similar users will sit close to each other in the graph. However, recent studies have revealed that real-world recommendation graphs are often heterophilous, i.e., dissimilar users will also often sit close to each other. One of the reasons for this heterophilia is shilling attacks that obscure the inherent characteristics of the graph and make the derived recommendations less accurate as a consequence. Hence, to cope with low homophily in recommender systems, we propose a recommendation model called PGT4Rec that is based on a Partitioned Graph Transformer. The model integrates label information into the learning process, which allows discriminative neighbourhoods of users to be generated. As such, the framework can both detect shilling attacks and predict user ratings for items. Extensive experiments on real and synthetic datasets show PGT4Rec as not only providing superior performance in these two tasks but also significant robustness to a range of adversarial conditions.
期刊介绍:
The IEEE Transactions on Knowledge and Data Engineering encompasses knowledge and data engineering aspects within computer science, artificial intelligence, electrical engineering, computer engineering, and related fields. It provides an interdisciplinary platform for disseminating new developments in knowledge and data engineering and explores the practicality of these concepts in both hardware and software. Specific areas covered include knowledge-based and expert systems, AI techniques for knowledge and data management, tools, and methodologies, distributed processing, real-time systems, architectures, data management practices, database design, query languages, security, fault tolerance, statistical databases, algorithms, performance evaluation, and applications.