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Self-Supervised Group Graph Collaborative Filtering for Group Recommendation

Published: 27 February 2023 Publication History

Abstract

Nowadays, it is more and more convenient for people to participate in group activities. Therefore, providing some recommendations to groups of individuals is indispensable. Group recommendation is the task of suggesting items or events for a group of users in social networks or online communities. In this work, we study group recommendation in a particular scenario, namely occasional group recommendation, which has few or no historical directly interacted items. Existing group recommendation methods mostly adopt attention-based preference aggregation strategies to capture group preferences. However, these models either ignore the complex high-order interactions between groups, users and items or greatly reduce the efficiency by introducing complex data structures. Moreover, occasional group recommendation suffers from the problem of data sparsity due to the lack of historical group-item interactions. In this work, we focus on addressing the aforementioned challenges and propose a novel group recommendation model called Self-Supervised Group Graph Collaborative Filtering (SGGCF). The goal of the model is capturing the high-order interactions between users, items and groups and alleviating the data sparsity issue in an efficient way. First, we explicitly model the complex relationships as a unified user-centered heterogeneous graph and devise a base group recommendation model. Second, we explore self-supervised learning on the graph with two kinds of contrastive learning module to capture the implicit relations between groups and items. At last, we treat the proposed contrastive learning loss as supplementary and apply a multi-task strategy to jointly train the BPR loss and the proposed contrastive learning loss. We conduct extensive experiments on three real-world datasets, and the experimental results demonstrate the superiority of our proposed model in comparison to the state-of-the-art baselines.

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Self-Supervised Group Graph Collaborative Filtering for Group Recommendation

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  • (2025)Hyperbolic Graph Contrastive Learning for Collaborative FilteringIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.352296037:3(1255-1267)Online publication date: Mar-2025
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  • (2024)Geography-aware Heterogeneous Graph Contrastive Learning for Travel RecommendationACM Transactions on Spatial Algorithms and Systems10.1145/364127710:3(1-22)Online publication date: 22-Jan-2024
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    cover image ACM Conferences
    WSDM '23: Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining
    February 2023
    1345 pages
    ISBN:9781450394079
    DOI:10.1145/3539597
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    Published: 27 February 2023

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    Author Tags

    1. collaborative filtering
    2. contrastive learning
    3. graph neural network
    4. recommender system

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    • (2025)Hyperbolic Graph Contrastive Learning for Collaborative FilteringIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.352296037:3(1255-1267)Online publication date: Mar-2025
    • (2024)A Federated Social Recommendation Approach with Enhanced Hypergraph Neural NetworkACM Transactions on Intelligent Systems and Technology10.1145/366593116:1(1-23)Online publication date: 30-Dec-2024
    • (2024)Geography-aware Heterogeneous Graph Contrastive Learning for Travel RecommendationACM Transactions on Spatial Algorithms and Systems10.1145/364127710:3(1-22)Online publication date: 22-Jan-2024
    • (2024)Swarm Self-supervised Hypergraph Embedding for RecommendationACM Transactions on Knowledge Discovery from Data10.1145/363805818:4(1-19)Online publication date: 13-Feb-2024
    • (2024)GraphPro: Graph Pre-training and Prompt Learning for RecommendationProceedings of the ACM on Web Conference 202410.1145/3589334.3645546(3690-3699)Online publication date: 13-May-2024
    • (2024)Tri-relational multi-faceted graph neural networks for automatic question taggingNeurocomputing10.1016/j.neucom.2024.127250576:COnline publication date: 25-Jun-2024
    • (2024)Group recommendation fueled by noise-based graph contrastive learningJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2024.10206336:5(102063)Online publication date: Jun-2024
    • (2024)Multiple Hypergraph Learning for Ephemeral Group RecommendationMachine Learning and Knowledge Discovery in Databases. Applied Data Science Track10.1007/978-3-031-70378-2_6(89-105)Online publication date: 22-Aug-2024
    • (2023)Contrastive Self-supervised Learning in Recommender Systems: A SurveyACM Transactions on Information Systems10.1145/362715842:2(1-39)Online publication date: 8-Nov-2023
    • (2023)MCRec: Multi-channel Gated Gifts Recommendation2023 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM58522.2023.00064(548-557)Online publication date: 1-Dec-2023
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