Multi-Granularity Click Confidence Learning via Self-Distillation in Recommendation

TL;DR

This paper introduces a self-distillation based method for learning multi-granularity click confidence in recommendation systems, improving user interest modeling without extra data or complex models.

Contribution

It proposes a novel self-distillation approach to estimate click confidence at multiple granularities, enhancing recommendation accuracy in real-world systems.

Findings

Significant improvements over various backbones in offline and online tests.

Deployed on a large-scale system affecting over 400 million users.

Effective modeling of user interests through multi-granularity confidence learning.

Abstract

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.