Score-based Generative Diffusion Models for Social Recommendations
Journal:
arXiv
Published Date:
Dec 20, 2024
Abstract
With the prevalence of social networks on online platforms, social
recommendation has become a vital technique for enhancing personalized
recommendations. The effectiveness of social recommendations largely relies on
the social homophily assumption, which presumes that individuals with social
connections often share similar preferences. However, this foundational premise
has been recently challenged due to the inherent complexity and noise present
in real-world social networks. In this paper, we tackle the low social
homophily challenge from an innovative generative perspective, directly
generating optimal user social representations that maximize consistency with
collaborative signals. Specifically, we propose the Score-based Generative
Model for Social Recommendation (SGSR), which effectively adapts the Stochastic
Differential Equation (SDE)-based diffusion models for social recommendations.
To better fit the recommendation context, SGSR employs a joint curriculum
training strategy to mitigate challenges related to missing supervision signals
and leverages self-supervised learning techniques to align knowledge across
social and collaborative domains. Extensive experiments on real-world datasets
demonstrate the effectiveness of our approach in filtering redundant social
information and improving recommendation performance.
