提升社交推荐多样性，同时不牺牲准确率。
Leave No One Behind: Enhancing Diversity While Maintaining Accuracy in Social Recommendation
- 用关系知识蒸馏，从非社交模型迁移高多样性结构知识。
- 在三个基准数据集上，多样性显著提升且准确率保持竞争力。
- 轻量级框架，可无缝集成到现有社交推荐系统中。
社交推荐通过融合用户社交关系，有效提升了推荐准确率。然而，除准确率外，多样性对提升用户参与度同样关键。尽管重要，现有社交推荐模型对多样性的影响仍鲜有研究。本文系统评估了主流社交推荐算法在准确率与多样性两方面的表现，发现其虽提升准确率，却常导致多样性下降。为此，我们提出多样化社交推荐（DivSR），采用关系知识蒸馏，将非社交推荐模型中的高多样性结构知识迁移到社交推荐模型中。DivSR为轻量级、模型无关框架，可无缝嵌入现有架构。在三个基准数据集上的实验表明，DivSR显著提升多样性，同时保持良好准确率，实现更优的准确率-多样性权衡。代码与数据已公开于：https://github.com/ll0ruc/DivSR。
原文摘要 · Abstract (English)
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.
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