Popularity Bias Mitigation Based on Time Interval-Aware Data Augmentation for Sequential Recommendation
Wenxu Zhao, Yongkang Li, Yuheng Wu, Yuan Cui, Xiaona Xia · ACM Transactions on Intelligent Systems and Technology · 2026
Sequential recommendation models temporal patterns in user interaction sequences to capture dynamic preference changes. However, real-world user interaction data suffer from sparsity, hindering effective preference learning and limiting sequential recommendation model performance. Although existing studies employ time interval-aware data augmentation to address data sparsity, they inadequately mitigate popularity bias, leading to recommendations containing too many popular items. Consequently, this article proposes popularity bias mitigation based on time interval-aware data augmentation (TiPBMRec), which is described as a two-stage framework. In the first stage, TiPBMRec augments user interaction sequences using an item reshaper and a sequence refiner, dynamically generating augmented sequences. In the second stage, the augmented sequences are used to construct the time interval-aware dual-view graph and dual-channel conformity weight network, effectively capturing the changing patterns of user preferences. Experiments on real-world datasets demonstrate that TiPBMRec might be more optimal and achieve the popularity bias mitigation. This study provides a novel idea for exploring the combination of time interval-aware data augmentation and popularity bias mitigation. The related methods and conclusions might be valuable for enabling adaptive sequential recommendations.