Popularity and Interest Signal Detection for Sequential Recommendation Denoising

Abstract

Sequential recommender systems aim to learn user preferences through historical interaction sequences. User interactions are driven both by popular trends and personal interests, introducing two types of noise: popular choices triggered by conformist behavior and irrelevant terms that do not reflect the user’s true interests. These noises inhibit the model’s ability to learn user representations. However, the lack of explicit noise labels makes the sequence denoising problem extremely challenging. We propose a new sequence denoising model (PISD), which extracts both popularity signal and interest signal to accurately identify and remove noisy items while maximizing the retention of personalized preference information. This approach generates clean subsequences for sequential recommendation tasks. Extensive experiments on three publicly available datasets show that our model is compatible with most sequential recommendations and significantly outperforms state-of-the-art denoising methods.