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

TL;DR

This paper investigates the impact of social recommendation models on diversity and accuracy, revealing a trade-off, and proposes DivSR, a knowledge distillation method that improves diversity without sacrificing accuracy.

Contribution

The paper introduces DivSR, a novel, lightweight, model-agnostic framework that enhances diversity in social recommendation systems through relational knowledge distillation.

Findings

DivSR significantly improves diversity in social recommendation models.

DivSR maintains competitive accuracy while enhancing diversity.

Empirical results on benchmark datasets validate the effectiveness of DivSR.

Abstract

Social recommendation, which incorporates social connections into recommender systems, has proven effective in improving recommendation accuracy. However, beyond accuracy, diversity is also crucial for enhancing user engagement. Despite its importance, the impact of social recommendation models on diversity remains largely unexplored. In this study, we systematically examine the dual performance of existing social recommendation algorithms in terms of both accuracy and diversity. Our empirical analysis reveals a concerning trend: while social recommendation models enhance accuracy, they often reduce diversity. To address this issue, we propose Diversified Social Recommendation (DivSR), a novel approach that employs relational knowledge distillation to transfer high-diversity structured knowledge from non-social recommendation models to social recommendation models. DivSR is a lightweight, model-agnostic framework that seamlessly integrates with existing social recommendation architectures. Experiments on three benchmark datasets demonstrate that DivSR significantly enhances diversity while maintaining competitive accuracy, achieving a superior accuracy-diversity trade-off. Our code and data are publicly available at: https://github.com/ll0ruc/DivSR.