Debiasing Sequential Recommendation with Time-aware Inverse Propensity Scoring
#sequential recommendation #debiasing #inverse propensity scoring #time-aware #user behavior #recommendation accuracy #temporal bias
📌 Key Takeaways
- The article introduces a method to reduce bias in sequential recommendation systems.
- It proposes using time-aware inverse propensity scoring to adjust for temporal biases.
- This approach aims to improve recommendation accuracy by accounting for user behavior changes over time.
- The method addresses biases from evolving user preferences and item popularity shifts.
📖 Full Retelling
arXiv:2603.04986v1 Announce Type: cross
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 di
🏷️ Themes
Recommendation Systems, Bias Mitigation
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Original Source
--> Computer Science > Information Retrieval arXiv:2603.04986 [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 Abstract: Sequential Recommendation 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 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 . 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. Comments: 11 pages Subjects: Information Retrieval (cs.IR) ; Artificial Intelligence (cs.AI) Cite as: arXiv:260...
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