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 (\textbf{L}earning \textbf{S}ocial Graph for \textbf{I}nactive User \textbf{R}ecommendation) 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~\url{https://github.com/liun-online/LSIR}.

翻译：社交关系已被广泛引入推荐系统以缓解数据稀疏问题。然而，原始社交关系因质量低、数量不足，往往无法有效提升推荐效果，尤其对于交互项目有限的非活跃用户而言。本文提出一种名为LSIR（面向非活跃用户推荐的社交图学习）的新型社交推荐方法，该方法可学习针对社交推荐（尤其适用于非活跃用户）的最优社交图结构。LSIR通过递归聚合用户与项目嵌入以协同编码项目与用户特征，进而采用图结构学习（GSL）基于增强嵌入去除噪声连边并添加新边，从而优化原始用户-用户社交图。此外，模型训练过程中引入模仿学习，引导活跃用户模拟非活跃用户的行为，从而改进非活跃用户的新连边构建。在真实数据集上的大量实验表明，LSIR在非活跃用户推荐任务中NDCG指标最高提升达129.58%。我们的代码开源在~\url{https://github.com/liun-online/LSIR}。