  In the information age, recommendation systems are vital for efficiently
filtering information and identifying user preferences. Online social platforms
have enriched these systems by providing valuable auxiliary information.
Socially connected users are assumed to share similar preferences, enhancing
recommendation accuracy and addressing cold start issues. However, empirical
findings challenge the assumption, revealing that certain social connections
can actually harm system performance. Our statistical analysis indicates a
significant amount of noise in the social network, where many socially
connected users do not share common interests. To address this issue, we
propose an innovative \underline{I}nterest-aware \underline{D}enoising and
\underline{V}iew-guided \underline{T}uning (IDVT) method for the social
recommendation. The first ID part effectively denoises social connections.
Specifically, the denoising process considers both social network structure and
user interaction interests in a global view. Moreover, in this global view, we
also integrate denoised social information (social domain) into the propagation
of the user-item interactions (collaborative domain) and aggregate user
representations from two domains using a gating mechanism. To tackle potential
user interest loss and enhance model robustness within the global view, our
second VT part introduces two additional views (local view and dropout-enhanced
view) for fine-tuning user representations in the global view through
contrastive learning. Extensive evaluations on real-world datasets with varying
noise ratios demonstrate the superiority of IDVT over state-of-the-art social
recommendation methods.
