  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.
