Score-based Generative Diffusion Models for Social Recommendations

TL;DR

This paper introduces SGSR, a novel score-based generative diffusion model that enhances social recommendation by directly generating user representations, overcoming social homophily limitations and improving recommendation accuracy.

Contribution

It proposes a new diffusion-based generative model for social recommendation, incorporating curriculum training and self-supervised learning to better utilize social and collaborative data.

Findings

Outperforms existing methods on real-world datasets

Effectively filters redundant social information

Improves recommendation accuracy

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.