  User interests can be viewed over different time scales, mainly including
stable long-term preferences and changing short-term intentions, and their
combination facilitates the comprehensive sequential recommendation. However,
existing work that focuses on different time scales of user modeling has
ignored the negative effects of different time-scale noise, which hinders
capturing actual user interests and cannot be resolved by conventional
sequential denoising methods. In this paper, we propose a Long- and Short-term
Interest Denoising Network (LSIDN), which employs different encoders and
tailored denoising strategies to extract long- and short-term interests,
respectively, achieving both comprehensive and robust user modeling.
Specifically, we employ a session-level interest extraction and evolution
strategy to avoid introducing inter-session behavioral noise into long-term
interest modeling; we also adopt contrastive learning equipped with a
homogeneous exchanging augmentation to alleviate the impact of unintentional
behavioral noise on short-term interest modeling. Results of experiments on two
public datasets show that LSIDN consistently outperforms state-of-the-art
models and achieves significant robustness.
