2024/05/08 by Nian Liu, Liu, Nian, Shen Fan +15
Computer Science · #Advanced Graph Neural Networks #Computer science #FOS: Computer and information sciences #Graph #Information Retrieval (cs.IR) #Information retrieval #Machine Learning (cs.LG) #Recommender Systems and Techniques #Recommender system #Sentiment Analysis and Opinion Mining #Social and Information Networks (cs.SI) #Social graph #Social media #Theoretical computer science #World Wide Web
paper · pdf · doi:10.48550/arxiv.2405.05288
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2024/05/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Social relations have been widely incorporated into recommender systems to alleviate data sparsity problem. However, raw social relations don't always benefit recommendation due to their inferior quality and insufficient quantity, especially for inactive users, whose interacted items are limited. In this paper, we propose a novel social recommendation method called LSIR (Learning Social Graph for Inactive User Recommendation) that learns an optimal social graph structure for social recommendation, especially for inactive users. LSIR recursively aggregates user and item embeddings to collaboratively encode item and user features. Then, graph structure learning (GSL) is employed to refine the raw user-user social graph, by removing noisy edges and adding new edges based on the enhanced embeddings. Meanwhile, mimic learning is implemented to guide active users in mimicking inactive users during model training, which improves the construction of new edges for inactive users. Extensive experiments on real-world datasets demonstrate that LSIR achieves significant improvements of up to 129.58% on NDCG in inactive user recommendation. Our code is available at~\urlhttps://github.com/liun-online/LSIR.