Socially-aware Dual Contrastive Learning for Cold-Start Recommendation

Jing Du, Zesheng Ye, Lina Yao, Bin Guo, Zhiwen Yu

科研成果: 书/报告/会议事项章节会议稿件同行评审

36 引用 (Scopus)

摘要

Social recommendation with Graph Neural Networks(GNNs) learns to represent cold users by fusing user-user social relations with user-item interactions, thereby alleviating the cold-start problem associated with recommender systems. Despite being well adapted to social relations and user-item interactions, these supervised models are still susceptible to popularity bias. Contrastive learning helps resolve this dilemma by identifying the properties that distinguish positive from negative samples. In its previous combinations with recommender systems, social relationships and cold-start cases in this context are not considered. Also, they primarily focus on collaborative features between users and items, leaving the similarity between items under-utilized. In this work, we propose socially-aware dual contrastive learning for cold-start recommendation, where cold users can be modeled in the same way as warm users. To take full advantage of social relations, we create dynamic node embeddings for each user by aggregating information from different neighbors according to each different query item, in the form of user-item pairs. We further design a dual-branch self-supervised contrastive objective to account for user-item collaborative features and item-item mutual information, respectively. On one hand, our framework eliminates popularity bias with proper negative sampling in contrastive learning, without extra ground-truth supervision. On the other hand, we extend previous contrastive learning methods to provide a solution to cold-start problem with social relations included. Extensive experiments on two real-world social recommendation datasets demonstrate its effectiveness.

源语言英语
主期刊名SIGIR 2022 - Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
出版商Association for Computing Machinery, Inc
1927-1932
页数6
ISBN(电子版)9781450387323
DOI
出版状态已出版 - 6 7月 2022
活动45th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2022 - Madrid, 西班牙
期限: 11 7月 202215 7月 2022

出版系列

姓名SIGIR 2022 - Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

会议

会议45th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2022
国家/地区西班牙
Madrid
时期11/07/2215/07/22

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