Exposure-Debiased Sequential Recommendation via Preference Disentanglement

Sequential recommendation aims to learn temporal dependencies from users’ interaction histories to predict future items, capturing both evolving and stable preferences. However, existing mainstream methods typically train only on observed clicks and treat unexposed or unclicked items as uniform negatives, despite the fact that many items are actually shown on the platform but not selected, thereby creating substantial exposure bias that distorts preference learning. Moreover, user exposure histories are entangled with both long-term persistent preferences and short-term transient intents; without explicitly disentanglement and structured debiasing, recommendation performance will decline significantly. To tackle these issues, we propose DELINE, an exposure-debiased sequential recommendation framework based on explicit user preference disentanglement. Specifically, we model the complete user exposure sequence as an offline reinforcement learning trajectory, deploy a dynamic convolution to extract short-term transient preferences, and employ a causal self-attention to model the evolution of long-term persistent preferences, thereby achieving structured disentanglement of mixed interests. Additionally, we design an exposure-aware debiasing mechanism, which is applied separately to the short-term and long-term preference capture modules, capable of suppressing attention bias induced by frequently exposed popular items and preventing the amplification of such bias. Furthermore, we propose a long-term and short-term constrained autoregressive augmentation strategy that generates pseudo-interaction items aligned with user preferences, thereby alleviating data sparsity and enhancing model robustness. Extensive experiments on three public benchmark datasets demonstrate that DELINE achieves the state-of-the-art performance in both recommendation accuracy and debiasing effectiveness.