  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.
