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，原始社交关系因其质量低下和数量不足，往往无法有效提升推荐效果，尤其对于交互物品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，该方法可学习最优社交图结构以提升社交推荐性能，特别针对非活跃用户。LSIR通过递归聚合用户和物品嵌入，协同编码物品与用户特征。随后，采用图结构学习（GSL）基于增强后的嵌入精炼原始用户-用户社交图，具体操作包括移除噪声边并添加新边。同时，引入模仿学习机制，在模型训练过程中引导活跃用户模仿非活跃用户的行为模式，从而优化非活跃用户的新边构建。在真实数据集上的大量实验表明，LSIR在非活跃用户推荐场景下，NDCG指标最高提升129.58%。我们的代码开源在：\url{https://github.com/liun-online/LSIR}。