Denoising Long- and Short-term Interests for Sequential Recommendation
This addresses noise in user modeling for sequential recommendation, which is an incremental improvement over existing methods.
The paper tackles the problem of noise in user interest modeling for sequential recommendation by proposing LSIDN, which uses tailored denoising strategies for long- and short-term interests, resulting in consistent outperformance of state-of-the-art models and significant robustness on two public datasets.
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.