Leave No One Behind: Enhancing Diversity While Maintaining Accuracy in Social Recommendation

Social recommendation, a branch of algorithms that utilizes social connection information to construct recommender systems, has demonstrated its effectiveness in enhancing recommendation accuracy. However, apart from accuracy, the diversity of recommendations also plays a critical role in user engagement. Unfortunately, the impact of social recommendation models on recommendation diversity remains largely unexplored. In this study, we investigate the dual performance of existing social recommendation algorithms in terms of accuracy and diversity. Our empirical findings highlight a concerning trend: social recommendation models tend to decrease diversity, despite their accuracy improvements. To address this issue, we propose a novel approach called Diversified Social Recommendation (DivSR), which leverages relational knowledge distillation techniques to transfer high-diversity structured knowledge from non-social recommendation models to social recommendation models. DivSR is designed as a simple, model-agnostic framework that integrates seamlessly with existing social recommendation architectures. Experimental results on three benchmark datasets demonstrate that DivSR significantly increases diversity without markedly compromising accuracy across various social recommendation backbones, achieving a better accuracy-diversity trade-off. Our code and data are publicly available at: https://github.com/ll0ruc/DivSR