Social recommendation has emerged as a powerful approach to enhance personalized recommendations by leveraging the social connections among users, such as following and friend relations observed in online social platforms. The fundamental assumption of social recommendation is that socially-connected users exhibit homophily in their preference patterns. This means that users connected by social ties tend to have similar tastes in user-item activities, such as rating and purchasing. However, this assumption is not always valid due to the presence of irrelevant and false social ties, which can contaminate user embeddings and adversely affect recommendation accuracy. To address this challenge, we propose a novel diffusion-based social denoising framework for recommendation (RecDiff). Our approach utilizes a simple yet effective hidden-space diffusion paradigm to alleivate the noisy effect in the compressed and dense representation space. By performing multi-step noise diffusion and removal, RecDiff possesses a robust ability to identify and eliminate noise from the encoded user representations, even when the noise levels vary. The diffusion module is optimized in a downstream task-aware manner, thereby maximizing its ability to enhance the recommendation process. We conducted extensive experiments to evaluate the efficacy of our framework, and the results demonstrate its superiority in terms of recommendation accuracy, training efficiency, and denoising effectiveness. The source code for the model implementation is publicly available at: https://github.com/HKUDS/RecDiff.

翻译：社交推荐通过利用用户间的社交连接（如在线社交平台中的关注和好友关系）来增强个性化推荐，已成为一种强有力的方法。社交推荐的基本假设是，社交连接的用户在其偏好模式上表现出同质性。这意味着通过社交纽带连接的用户在用户-项目活动（如评分和购买）中往往具有相似的品味。然而，由于存在无关和虚假的社交纽带，这一假设并非总是成立，这些噪声会污染用户嵌入并对推荐准确性产生不利影响。为应对这一挑战，我们提出了一种新颖的基于扩散的社交去噪推荐框架（RecDiff）。我们的方法利用一种简单而有效的隐空间扩散范式，以减轻在压缩且稠密的表示空间中的噪声效应。通过执行多步噪声扩散与去除，RecDiff具备强大的能力来识别并消除编码后用户表示中的噪声，即使噪声水平发生变化。扩散模块以任务感知的方式进行下游优化，从而最大化其增强推荐过程的能力。我们进行了广泛的实验来评估该框架的有效性，结果表明其在推荐准确性、训练效率和去噪效果方面均具有优越性。模型实现的源代码公开于：https://github.com/HKUDS/RecDiff。