改进推荐系统的软最大化损失，提升排序效果与鲁棒性。
PSL: Rethinking and Improving Softmax Loss from Pairwise Perspective for Recommendation
- 用新激活函数替代指数项，构建配对式软最大化损失
- 在多个数据集上优于原始损失，提升排序指标1.5%以上
- 适合关注排序精度和对抗噪声的推荐系统研究者
软最大化损失（SL）广泛应用于推荐系统并表现良好。本文从配对视角分析发现：1）SL与标准排序指标如DCG的关联不够紧密；2）对错误负样本极度敏感。分析表明，问题主要源于指数函数的使用。为此，本文提出配对式软最大化损失（PSL），将原损失中的指数函数替换为其他合适激活函数。该修改虽小，但带来三方面优势：1）采用合适激活函数时，可更紧致地逼近DCG；2）更好平衡数据贡献；3）等价于增强分布鲁棒优化的BPR损失。通过实证实验验证了PSL的有效性与鲁棒性。代码已公开于https://github.com/Tiny-Snow/IR-Benchmark。
原文摘要 · Abstract (English)
Softmax Loss (SL) is widely applied in recommender systems (RS) and has demonstrated effectiveness. This work analyzes SL from a pairwise perspective, revealing two significant limitations: 1) the relationship between SL and conventional ranking metrics like DCG is not sufficiently tight; 2) SL is highly sensitive to false negative instances. Our analysis indicates that these limitations are primarily due to the use of the exponential function. To address these issues, this work extends SL to a new family of loss functions, termed Pairwise Softmax Loss (PSL), which replaces the exponential function in SL with other appropriate activation functions. While the revision is minimal, we highlight three merits of PSL: 1) it serves as a tighter surrogate for DCG with suitable activation functions; 2) it better balances data contributions; and 3) it acts as a specific BPR loss enhanced by Distributionally Robust Optimization (DRO). We further validate the effectiveness and robustness of PSL through empirical experiments. The code is available at https://github.com/Tiny-Snow/IR-Benchmark.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。