Sequential Recommendation (SR) predicts users next interactions by modeling the temporal order of their historical behaviors. Existing approaches, including traditional sequential models and generative recommenders, achieve strong performance but primarily rely on explicit interactions such as clicks or purchases while overlooking item exposures. This ignorance introduces selection bias, where exposed but unclicked items are misinterpreted as disinterest, and exposure bias, where unexposed items are treated as irrelevant. Effectively addressing these biases requires distinguishing between items that were "not exposed" and those that were "not of interest", which cannot be reliably inferred from correlations in historical data. Counterfactual reasoning provides a natural solution by estimating user preferences under hypothetical exposure, and Inverse Propensity Scoring (IPS) is a common tool for such estimation. However, conventional IPS methods are static and fail to capture the sequential dependencies and temporal dynamics of user behavior. To overcome these limitations, we propose Time aware Inverse Propensity Scoring (TIPS). Unlike traditional static IPS, TIPS effectively accounts for sequential dependencies and temporal dynamics, thereby capturing user preferences more accurately. Extensive experiments show that TIPS consistently enhances recommendation performance as a plug-in for various sequential recommenders. Our code will be publicly available upon acceptance.

翻译：序列推荐通过建模用户历史行为的时间顺序来预测其下一次交互。现有方法，包括传统序列模型和生成式推荐器，虽然取得了强劲性能，但主要依赖点击或购买等显式交互，而忽略了物品曝光。这种忽略导致了选择偏差（即被曝光但未点击的物品被误判为用户不感兴趣）和曝光偏差（即未曝光的物品被视为无关）。有效解决这些偏差需要区分“未曝光”物品与“不感兴趣”物品，而仅从历史数据的相关性中无法可靠推断此信息。反事实推理通过估计假设曝光下的用户偏好为此提供了自然解决方案，逆倾向性评分是此类估计的常用工具。然而，传统IPS方法是静态的，无法捕捉用户行为的序列依赖性和时序动态性。为克服这些局限，我们提出了时序感知逆倾向性评分。与传统静态IPS不同，TIPS能有效考量序列依赖和时序动态，从而更准确地捕捉用户偏好。大量实验表明，TIPS作为多种序列推荐器的插件，能持续提升推荐性能。我们的代码将在论文录用后公开。