Debiasing Sequential Recommendation with Time-aware Inverse Propensity Scoring
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 inferre...
| Authors | Sirui Huang, Jing Long, Qian Li et al. |
|---|---|
| Primary category | cs.IR |
| Published | 5 March 2026 |
| Last updated at source | 5 March 2026 |
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