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<p>In recent times, online social networks have significantly enhanced user experiences, with social recommendation systems facilitating easier discovery of relevant information. Advanced graph neural network-based social recommendation approaches are now incorporating higher-order social relations, such as connections between friends of friends, to uncover user preferences. However, current high-order methods overlook implicit heterogeneous social connections among users and fail to account for the dynamic evolution of user interests over time. To address this issue, the paper proposes a novel heterogeneous hypergraph model for an enhanced social recommendation. Specifically, this methodology effectively manages intricate social relationships by mining various heterogeneous preferences, incorporating user–user, user–item, and item–item interactions mining represented by knowledge hypergraph-based interactions through hypergraph convolution network (HGCN). By employing HGCN, the approach aims to amplify the impact of mined heterogeneous preferences that exhibit significantly high-order user social connections, both explicit and implicit, thereby enhancing the representations by employing hypergraph convolution neural networks to provide recommendations. Further, the absence of social data for certain users is addressed by integrating implicit social connections derived from the various heterogeneous preferences and with explicit social relationships sourced from the item–item similarity matrix represented through the hypergraph-driven heterogeneous preference model (HDHP) model. Comprehensive experimentation is conducted on three real-world datasets to showcase the efficacy of the proposed HDHP model in comparison to existing state-of-the-art techniques. The proposed model shows 0.8768, 0.8876, 0.9099, 0.4554, 0.4201, and 0.4756 of recall@10, 20, 50, and NDGC@10, 20, 50, respectively. The proposed model shows an 83.95% enhancement in recall@20 and a 26.68% improvement in NDGC compared to the Dual Channel Hypergraph Collaborative Filtering (DHCF) state-of-the-art model when trained on the MovieLens 100&#xa0;k dataset.</p>

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A novel heterogeneous hypergraph social network recommendation system

  • 期刊论文

  • 发表日期：

    2026年2月4日

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  • Rakshita Mall,
  • Maheshwari Prasad Singh

摘要

In recent times, online social networks have significantly enhanced user experiences, with social recommendation systems facilitating easier discovery of relevant information. Advanced graph neural network-based social recommendation approaches are now incorporating higher-order social relations, such as connections between friends of friends, to uncover user preferences. However, current high-order methods overlook implicit heterogeneous social connections among users and fail to account for the dynamic evolution of user interests over time. To address this issue, the paper proposes a novel heterogeneous hypergraph model for an enhanced social recommendation. Specifically, this methodology effectively manages intricate social relationships by mining various heterogeneous preferences, incorporating user–user, user–item, and item–item interactions mining represented by knowledge hypergraph-based interactions through hypergraph convolution network (HGCN). By employing HGCN, the approach aims to amplify the impact of mined heterogeneous preferences that exhibit significantly high-order user social connections, both explicit and implicit, thereby enhancing the representations by employing hypergraph convolution neural networks to provide recommendations. Further, the absence of social data for certain users is addressed by integrating implicit social connections derived from the various heterogeneous preferences and with explicit social relationships sourced from the item–item similarity matrix represented through the hypergraph-driven heterogeneous preference model (HDHP) model. Comprehensive experimentation is conducted on three real-world datasets to showcase the efficacy of the proposed HDHP model in comparison to existing state-of-the-art techniques. The proposed model shows 0.8768, 0.8876, 0.9099, 0.4554, 0.4201, and 0.4756 of recall@10, 20, 50, and NDGC@10, 20, 50, respectively. The proposed model shows an 83.95% enhancement in recall@20 and a 26.68% improvement in NDGC compared to the Dual Channel Hypergraph Collaborative Filtering (DHCF) state-of-the-art model when trained on the MovieLens 100 k dataset.