[2603.04986] Debiasing Sequential Recommendation with Time-aware Inverse Propensity Scoring

Computer Science > Information Retrieval arXiv:2603.04986 (cs) [Submitted on 5 Mar 2026] Title:Debiasing Sequential Recommendation with Time-aware Inverse Propensity Scoring Authors:Sirui Huang, Jing Long, Qian Li, Guandong Xu, Qing Li View a PDF of the paper titled Debiasing Sequential Recommendation with Time-aware Inverse Propensity Scoring, by Sirui Huang and 4 other authors View PDF HTML (experimental) Abstract: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 l...