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Swarm Self-supervised Hypergraph Embedding for Recommendation

Published: 13 February 2024 Publication History

Abstract

The information era brings both opportunities and challenges to information services. Confronting information overload, recommendation technology is dedicated to filtering personalized content to meet users’ requirements. The extremely sparse interaction records and their imbalanced distribution become a big obstacle to building a high-quality recommendation model. In this article, we propose a swarm self-supervised hypergraph embedding (SHE) model to predict users’ interests by hypergraph convolution and self-supervised discrimination. SHE builds a hypergraph with multiple interest clues to alleviate the interaction sparsity issue and performs interest propagation to embed CF signals in hybrid learning on the hypergraph. It follows an auxiliary local view by similar hypergraph construction and interest propagation to restrain unnecessary propagation between user swarms. Besides, interest contrast further inserts self-discrimination to deal with long-tail bias issue and enhance interest modeling, which aid recommendation by a multi-task learning optimization. Experiments on public datasets show that the proposed SHE outperforms the state-of-the-art models demonstrating the effectiveness of hypergraph-based interest propagation and swarm-aware interest contrast to enhance embedding for recommendation.

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  • (2024)Research on Personalized Recommendation of Complementary Products Based on Demand Cross-Elasticity and HypergraphsElectronics10.3390/electronics1323485113:23(4851)Online publication date: 9-Dec-2024

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    Published In

    cover image ACM Transactions on Knowledge Discovery from Data
    ACM Transactions on Knowledge Discovery from Data  Volume 18, Issue 4
    May 2024
    707 pages
    EISSN:1556-472X
    DOI:10.1145/3613622
    Issue’s Table of Contents

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 13 February 2024
    Online AM: 20 December 2023
    Accepted: 15 December 2023
    Revised: 17 October 2023
    Received: 08 June 2023
    Published in TKDD Volume 18, Issue 4

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    Author Tags

    1. Recommendation
    2. user interest
    3. hypergraph
    4. graph convolution
    5. contrastive learning

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    • National Natural Science Foundation of China

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    • (2024)Research on Personalized Recommendation of Complementary Products Based on Demand Cross-Elasticity and HypergraphsElectronics10.3390/electronics1323485113:23(4851)Online publication date: 9-Dec-2024

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