Social recommender systems facilitate social connections by identifying potential friends for users. Each user maintains a local social network centered around themselves, resulting in a naturally distributed social structure. Recent research on distributed modeling for social recommender systems has gained increasing attention, as it naturally aligns with the user-centric structure of user interactions. Current distributed social recommender systems rely on automatically combining predictions from multiple models, often overlooking the user's active role in validating whether suggested connections are appropriate. Moreover, recommendation decisions are validated by individual users rather than derived from a single global ordering of candidates. As a result, standard ranking-based evaluation metrics make it difficult to evaluate whether a user-confirmed recommendation decision is actually correct. To address these limitations, we propose DeSocial, a distributed social recommendation framework with user-validation. DeSocial enables users to select recommendation algorithms to validate their potential connections, and the verification is processed through majority consensus among multiple independent user validators. To evaluate the distributed recommender system with user validator, we formulate this setting as a link prediction and verification task and introduce Acc@K, a consensus-based evaluation metric that measures whether user-approved recommendations are correct. Experiments on 4 real-world social networks shows that DeSocial improves decision correctness and robustness compared to single-point and distributed baselines. These findings highlight the potential of user-validated distributed recommender systems as a practical approach to social recommendation, with broader applicability to distributed and decentralized recommendations. Code: https://github.com/agiresearch/DeSocial.

翻译：社交推荐系统通过为用户识别潜在朋友来促进社交连接。每个用户维护一个以自身为中心的本地社交网络，从而形成天然的分布式社交结构。社交推荐系统的分布式建模研究近年来日益受到关注，因为它天然契合用户交互中以用户为中心的结构。当前的分布式社交推荐系统依赖于自动组合多个模型的预测结果，往往忽视了用户在验证推荐连接是否合适方面的主动作用。此外，推荐决策由个体用户验证，而非源自候选人的单一全局排序。因此，基于排序的标准评估指标难以评估用户确认的推荐决策是否正确。为应对这些局限，我们提出DeSocial——一个具有用户验证功能的分布式社交推荐框架。DeSocial使用户能够选择推荐算法来验证其潜在连接，并通过多个独立用户验证者之间的多数共识进行处理。为评估带有用户验证器的分布式推荐系统，我们将此场景形式化为链接预测与验证任务，并引入Acc@K这一基于共识的评估指标，用于衡量用户批准的推荐是否正确。在4个真实社交网络上的实验表明，与单点及分布式基线方法相比，DeSocial提升了决策正确性和鲁棒性。这些发现凸显了用户验证的分布式推荐系统作为社交推荐实用方法的潜力，并具备向分布式及去中心化推荐场景更广泛应用的可行性。代码：https://github.com/agiresearch/DeSocial。