Denoised Self-Augmented Learning for Social Recommendation

TL;DR

This paper introduces DSL, a denoised self-augmented learning framework for social recommendation that effectively filters noise in social data and improves recommendation accuracy through adaptive semantic alignment.

Contribution

The paper proposes a novel DSL model that preserves helpful social relations and enables personalized knowledge transfer, addressing social data noise in recommendation systems.

Findings

DSL outperforms state-of-the-art methods on benchmark datasets.

The model effectively filters noisy social connections.

Adaptive semantic alignment enhances user preference modeling.

Abstract

Social recommendation is gaining increasing attention in various online applications, including e-commerce and online streaming, where social information is leveraged to improve user-item interaction modeling. Recently, Self-Supervised Learning (SSL) has proven to be remarkably effective in addressing data sparsity through augmented learning tasks. Inspired by this, researchers have attempted to incorporate SSL into social recommendation by supplementing the primary supervised task with social-aware self-supervised signals. However, social information can be unavoidably noisy in characterizing user preferences due to the ubiquitous presence of interest-irrelevant social connections, such as colleagues or classmates who do not share many common interests. To address this challenge, we propose a novel social recommender called the Denoised Self-Augmented Learning paradigm (DSL). Our model not only preserves helpful social relations to enhance user-item interaction modeling but also enables personalized cross-view knowledge transfer through adaptive semantic alignment in embedding space. Our experimental results on various recommendation benchmarks confirm the superiority of our DSL over state-of-the-art methods. We release our model implementation at: https://github.com/HKUDS/DSL.