Click-through rate (CTR) prediction holds significant importance in the realm of online advertising. While many existing approaches treat it as a binary classification problem and utilize binary cross entropy (BCE) as the optimization objective, recent advancements have indicated that combining BCE loss with ranking loss yields substantial performance improvements. However, the full efficacy of this combination loss remains incompletely understood. In this paper, we uncover a new challenge associated with BCE loss in scenarios with sparse positive feedback, such as CTR prediction: the gradient vanishing for negative samples. Subsequently, we introduce a novel perspective on the effectiveness of ranking loss in CTR prediction, highlighting its ability to generate larger gradients on negative samples, thereby mitigating their optimization issues and resulting in improved classification ability. Our perspective is supported by extensive theoretical analysis and empirical evaluation conducted on publicly available datasets. Furthermore, we successfully deployed the ranking loss in Tencent's online advertising system, achieving notable lifts of 0.70% and 1.26% in Gross Merchandise Value (GMV) for two main scenarios. The code for our approach is openly accessible at the following GitHub repository: https://github.com/SkylerLinn/Understanding-the-Ranking-Loss.

中文翻译：

---

了解稀疏用户反馈的推荐排名损失

点击率 (CTR) 预测在在线广告领域具有重要意义。虽然许多现有方法将其视为二元分类问题并利用二元交叉熵 (BCE) 作为优化目标，但最近的进展表明，将 BCE 损失与排名损失相结合可以带来显着的性能改进。然而，这种组合损失的全部功效仍不完全清楚。在本文中，我们发现了在具有稀疏正反馈的场景（例如 CTR 预测）中与 BCE 损失相关的新挑战：负样本的梯度消失。随后，我们介绍了排名损失在 CTR 预测中的有效性的新视角，强调其在负样本上生成更大梯度的能力，从而减轻其优化问题并提高分类能力。我们的观点得到了对公开数据集进行的广泛理论分析和实证评估的支持。此外，我们成功将排名损失部署在腾讯网络广告系统中，实现两个主要场景的商品总价值（GMV）显着提升0.70%和1.26%。我们方法的代码可以在以下 GitHub 存储库中公开访问：https://github.com/SkylerLinn/Understanding-the-Ranking-Loss。