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Socially-aware Dual Contrastive Learning for Cold-Start Recommendation

Published: 07 July 2022 Publication History

Abstract

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.

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cover image ACM Conferences
SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
July 2022
3569 pages
ISBN:9781450387323
DOI:10.1145/3477495
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 07 July 2022

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Author Tags

  1. cold-start
  2. contrastive learning
  3. social recommendation

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  • Chinese Scholarship Council

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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  • (2025)Explainable robo-advisor: An online learning framework for new investors without trading recordsNeurocomputing10.1016/j.neucom.2025.129463(129463)Online publication date: Jan-2025
  • (2024)Multi-Agent Attacks for Black-Box Social RecommendationsACM Transactions on Information Systems10.1145/369610543:1(1-26)Online publication date: 21-Oct-2024
  • (2024)Dual Contrastive Learning for Cross-Domain Named Entity RecognitionACM Transactions on Information Systems10.1145/367887942:6(1-33)Online publication date: 18-Oct-2024
  • (2024)A Survey of Graph Neural Networks for Social Recommender SystemsACM Computing Surveys10.1145/366182156:10(1-34)Online publication date: 22-Jun-2024
  • (2024)RecDiff: Diffusion Model for Social RecommendationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679630(1346-1355)Online publication date: 21-Oct-2024
  • (2024)CMCLRec: Cross-modal Contrastive Learning for User Cold-start Sequential RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657839(1589-1598)Online publication date: 10-Jul-2024
  • (2024)SSLRec: A Self-Supervised Learning Framework for RecommendationProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635814(567-575)Online publication date: 4-Mar-2024
  • (2024)Mining Relational Similarity in Social Networks for Enhanced Recommendations2024 IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA)10.1109/ISPA63168.2024.00020(90-97)Online publication date: 30-Oct-2024
  • (2024)Inhomogeneous Interest Modeling via Hypergraph Convolutional Networks for Social Recommendation2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10651102(1-9)Online publication date: 30-Jun-2024
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