Recommendation systems rely on historical clicks to learn user interests and provide appropriate items. However, current studies tend to treat clicks equally, which may ignore the assorted intensities of user interests in different clicks. In this paper, we aim to achieve multi-granularity Click confidence Learning via Self-Distillation in recommendation (CLSD). Due to the lack of supervised signals in click confidence, we first apply self-supervised learning to obtain click confidence scores via a global self-distillation method. After that, we define a local confidence function to adapt confidence scores at the user group level, since the confidence distributions can be varied among user groups. With the combination of multi-granularity confidence learning, we can distinguish the quality of clicks and model user interests more accurately without involving extra data and model structures. The significant improvements over different backbones on industrial offline and online experiments in a real-world recommender system prove the effectiveness of our model. Recently, CLSD has been deployed on a large-scale recommender system, affecting over 400 million users.

翻译：推荐系统依赖历史点击来学习用户兴趣并提供合适物品。然而，现有研究通常将点击视为同等重要，这可能会忽略不同点击中用户兴趣的强度差异。本文旨在通过推荐中的自蒸馏方法实现多粒度点击置信度学习（CLSD）。由于点击置信度缺乏监督信号，我们首先应用自监督学习，通过全局自蒸馏方法获取点击置信度分数。随后，我们定义了局部置信度函数，在用户群体层面调整置信度分数，因为不同用户群体的置信度分布可能存在差异。通过结合多粒度置信度学习，我们无需引入额外数据和模型结构，即可更准确地区分点击质量并建模用户兴趣。在真实推荐系统的工业离线与在线实验中，基于不同骨干网络的显著改进验证了模型的有效性。近期，CLSD已部署于大规模推荐系统，覆盖超过4亿用户。