提出新损失函数DrRL，提升推荐系统准确率与鲁棒性。
Advancing Loss Functions in Recommender Systems: A Comparative Study with a Rényi Divergence-Based Solution
- 用Rényi散度改进分布鲁棒优化，统一软标签与余弦对比损失
- 在多个数据集上验证，相比SL和CCL提升推荐准确率与抗干扰能力
- 适合关注推荐模型优化与泛化性能的研究者
损失函数在推荐系统优化中起关键作用。软标签损失（SL）和余弦对比损失（CCL）尤为有效，二者在理论上有深层联系但存在差异。本研究深入分析发现：1）两者均可视为传统损失的分布鲁棒优化（DRO）增强，提升了对分布偏移的鲁棒性；2）各自存在局限——SL对误负例敏感，而CCL数据利用率低。为此，本文提出新损失函数DrRL，通过在DRO中引入Rényi散度，统一并融合SL与CCL的优势结构，可有效缓解上述缺陷。大量实验表明，DrRL在推荐准确率与鲁棒性方面均优于现有方法。
原文摘要 · Abstract (English)
Loss functions play a pivotal role in optimizing recommendation models. Among various loss functions, Softmax Loss (SL) and Cosine Contrastive Loss (CCL) are particularly effective. Their theoretical connections and differences warrant in-depth exploration. This work conducts comprehensive analyses of these losses, yielding significant insights: 1) Common strengths -- both can be viewed as augmentations of traditional losses with Distributional Robust Optimization (DRO), enhancing robustness to distributional shifts; 2) Respective limitations -- stemming from their use of different distribution distance metrics in DRO optimization, SL exhibits high sensitivity to false negative instances, whereas CCL suffers from low data utilization. To address these limitations, this work proposes a new loss function, DrRL, which generalizes SL and CCL by leveraging Rényi-divergence in DRO optimization. DrRL incorporates the advantageous structures of both SL and CCL, and can be demonstrated to effectively mitigate their limitations. Extensive experiments have been conducted to validate the superiority of DrRL on both recommendation accuracy and robustness.
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