Riwei Lai, Shitong Xiao, R. Chen, Li Chen, Qilong Han, Li Li
{"title":"多邻居关系增强图协同过滤","authors":"Riwei Lai, Shitong Xiao, R. Chen, Li Chen, Qilong Han, Li Li","doi":"10.1109/WI-IAT55865.2022.00016","DOIUrl":null,"url":null,"abstract":"Graph convolutional networks (GCNs) have substantially advanced state-of-the-art collaborative filtering (CF) methods. Recent GCN-based CF methods have started to explore potential neighbor relations instead of only focusing on direct user-item interactions. Despite the encouraging progress, they still suffer from two notable limitations: (1) only one type of potential neighbor relations is explored, i.e., co-interacting with the same item/user, neglecting the fact that user-item interactions are associated with various attributes and thus there can exist multiple potential neighbor relations from different aspects; (2) the distinction between information from direct user-item interactions and potential neighbor relations and their different extents of influence are not fully considered, which represent very different aspects of a user or an item. In this paper, we propose a novel Multiple Neighbor Relation enhanced method for Graph Collaborative Filtering (MNR-GCF) to address these two limitations. First, in order to capture multiple potential neighbor relations, we introduce a new construction of heterogeneous information networks with multiple types of edges to account for multiple neighbor relations, and a multi-relation aggregation mechanism to effectively integrate relation-aware information. We then enhance CF with a degree-aware dynamic routing mechanism to dynamically and adaptively fuse information from direct user-item interactions and potential neighbor relations at each aggregation layer. Our extensive experimental results show that our solution consistently and substantially outperforms a large number of state-of-the-art CF methods on three public benchmark datasets.","PeriodicalId":345445,"journal":{"name":"2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multiple Neighbor Relation Enhanced Graph Collaborative Filtering\",\"authors\":\"Riwei Lai, Shitong Xiao, R. Chen, Li Chen, Qilong Han, Li Li\",\"doi\":\"10.1109/WI-IAT55865.2022.00016\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Graph convolutional networks (GCNs) have substantially advanced state-of-the-art collaborative filtering (CF) methods. Recent GCN-based CF methods have started to explore potential neighbor relations instead of only focusing on direct user-item interactions. Despite the encouraging progress, they still suffer from two notable limitations: (1) only one type of potential neighbor relations is explored, i.e., co-interacting with the same item/user, neglecting the fact that user-item interactions are associated with various attributes and thus there can exist multiple potential neighbor relations from different aspects; (2) the distinction between information from direct user-item interactions and potential neighbor relations and their different extents of influence are not fully considered, which represent very different aspects of a user or an item. In this paper, we propose a novel Multiple Neighbor Relation enhanced method for Graph Collaborative Filtering (MNR-GCF) to address these two limitations. First, in order to capture multiple potential neighbor relations, we introduce a new construction of heterogeneous information networks with multiple types of edges to account for multiple neighbor relations, and a multi-relation aggregation mechanism to effectively integrate relation-aware information. We then enhance CF with a degree-aware dynamic routing mechanism to dynamically and adaptively fuse information from direct user-item interactions and potential neighbor relations at each aggregation layer. Our extensive experimental results show that our solution consistently and substantially outperforms a large number of state-of-the-art CF methods on three public benchmark datasets.\",\"PeriodicalId\":345445,\"journal\":{\"name\":\"2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WI-IAT55865.2022.00016\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WI-IAT55865.2022.00016","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Graph convolutional networks (GCNs) have substantially advanced state-of-the-art collaborative filtering (CF) methods. Recent GCN-based CF methods have started to explore potential neighbor relations instead of only focusing on direct user-item interactions. Despite the encouraging progress, they still suffer from two notable limitations: (1) only one type of potential neighbor relations is explored, i.e., co-interacting with the same item/user, neglecting the fact that user-item interactions are associated with various attributes and thus there can exist multiple potential neighbor relations from different aspects; (2) the distinction between information from direct user-item interactions and potential neighbor relations and their different extents of influence are not fully considered, which represent very different aspects of a user or an item. In this paper, we propose a novel Multiple Neighbor Relation enhanced method for Graph Collaborative Filtering (MNR-GCF) to address these two limitations. First, in order to capture multiple potential neighbor relations, we introduce a new construction of heterogeneous information networks with multiple types of edges to account for multiple neighbor relations, and a multi-relation aggregation mechanism to effectively integrate relation-aware information. We then enhance CF with a degree-aware dynamic routing mechanism to dynamically and adaptively fuse information from direct user-item interactions and potential neighbor relations at each aggregation layer. Our extensive experimental results show that our solution consistently and substantially outperforms a large number of state-of-the-art CF methods on three public benchmark datasets.