Graph Convolutional Network-Based Interest Recommendation in the Social Internet of Things With Sparse Social Interaction

With the emergence of information overload in the Social Internet of Things (SIoT), personalized recommender systems have become essential for helping users locate the items they need. Effectively modeling the heterogeneous relationships across multiple information sources and weighting them according to their varying importance, especially under sparse social interactions in the SIoT, remains a key challenge. To address this issue, this paper proposes a Multi-source Adaptive Relational Graph Convolutional Network (MARGCN) framework for recommender systems. Useruser and useritem interactions are modeled as two relational graphs, and their embedded features are aggregated using graph convolutional networks (GCNs). A multi-source relationship perception model is designed to dynamically perceive and measure the importance of multiple relationships within the graph structure, thereby enhancing the ability to recognize heterogeneous information. An adaptive information fusion model is then constructed to dynamically integrate representations from different sources through a learnable fusion strategy, avoiding information loss or redundancy caused by simple weighting. Users and items are ultimately represented by aggregating and updating their embeddings via GCNs. Experiments show that compared with the most advanced methods, MARGCN improves the hit rate (HR) and the normalized discounted cumulative gain (NDCG) by 2.66%, 0.52%, and 3.93% in HR@5, HR@10, and HR@15 and by 1.58%, 1.32%, and 3.14% in NDCG@5, NDCG@10, and NDCG@15, respectively.