Debiasing Sequential Recommendation with Time-aware Inverse Propensity Scoring

• URL: http://arxiv.org/abs/2603.04986v1
• Date: Thu, 05 Mar 2026 09:25:19 GMT
• Title: Debiasing Sequential Recommendation with Time-aware Inverse Propensity Scoring
• Authors: Sirui Huang, Jing Long, Qian Li, Guandong Xu, Qing Li,
• Abstract summary: Sequential Recommendation predicts users next interactions by modeling the temporal order of their historical behaviors.<n>We propose Time aware Inverse Propensity Scoring (TIPS) to capture user preferences more accurately.<n>TIPS consistently enhances recommendation performance as a plug-in for various sequential recommenders.
• Score: 14.934335528596067
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

Related papers

• Quantifying the Potential to Escape Filter Bubbles: A Behavior-Aware Measure via Contrastive Simulation [82.74100448579857]
  We introduce Bubble Escape Potential (BEP), a behavior-aware measure that quantifies how easily users can escape from filter bubbles.<n>We conduct extensive experiments across multiple recommendation models to examine the relationship between predictive accuracy and bubble escape potential across different groups.
  arXiv  Detail & Related papers  (2025-11-27T07:21:04Z)
• Time Matters: Enhancing Sequential Recommendations with Time-Guided Graph Neural ODEs [39.5138461997608]
  Sequential recommendation (SR) is widely deployed in e-commerce platforms, streaming services, etc.<n>Existing methods often overlook two critical factors: irregular user interests between interactions and highly uneven item distributions over time.<n>We propose TGODE to both enhance and capture the long-term historical interactions.
  arXiv  Detail & Related papers  (2025-11-23T08:46:27Z)
• Preference Trajectory Modeling via Flow Matching for Sequential Recommendation [50.077447974294586]
  Sequential recommendation predicts each user's next item based on their historical interaction sequence.<n>FlowRec is a simple yet effective sequential recommendation framework.<n>We construct a personalized behavior-based prior distribution to replace Gaussian noise and learn a vector field to model user preference trajectories.
  arXiv  Detail & Related papers  (2025-08-25T02:55:42Z)
• Future Sight and Tough Fights: Revolutionizing Sequential Recommendation with FENRec [31.264334651290437]
  Sequential recommendation (SR) systems predict user preferences by analyzing time-ordered interaction sequences.<n>A common challenge for SR is data sparsity, as users typically interact with only a limited number of items.<n>We propose Future data utilization with Enduring Negatives for contrastive learning in sequential Recommendation (FENRec)
  arXiv  Detail & Related papers  (2024-12-16T09:20:29Z)
• Sequential Recommendation on Temporal Proximities with Contrastive Learning and Self-Attention [3.7182810519704095]
  Sequential recommender systems identify user preferences from their past interactions to predict subsequent items optimally. Recent models often neglect similarities in users' actions that occur implicitly among users during analogous timeframes. We propose a sequential recommendation model called TemProxRec, which includes contrastive learning and self-attention methods to consider temporal proximities.
  arXiv  Detail & Related papers  (2024-02-15T08:33:16Z)
• Debiasing the Cloze Task in Sequential Recommendation with Bidirectional Transformers [0.0]
  We argue that Inverse Propensity Scoring (IPS) does not extend to sequential recommendation because it fails to account for the temporal nature of the problem. We then propose a novel propensity scoring mechanism, which can theoretically debias the Cloze task in sequential recommendation.
  arXiv  Detail & Related papers  (2023-01-22T21:44:25Z)
• Rethinking Missing Data: Aleatoric Uncertainty-Aware Recommendation [59.500347564280204]
  We propose a new Aleatoric Uncertainty-aware Recommendation (AUR) framework. AUR consists of a new uncertainty estimator along with a normal recommender model. As the chance of mislabeling reflects the potential of a pair, AUR makes recommendations according to the uncertainty.
  arXiv  Detail & Related papers  (2022-09-22T04:32:51Z)
• Cross Pairwise Ranking for Unbiased Item Recommendation [57.71258289870123]
  We develop a new learning paradigm named Cross Pairwise Ranking (CPR) CPR achieves unbiased recommendation without knowing the exposure mechanism. We prove in theory that this way offsets the influence of user/item propensity on the learning.
  arXiv  Detail & Related papers  (2022-04-26T09:20:27Z)
• Sequential Recommendation via Stochastic Self-Attention [68.52192964559829]
  Transformer-based approaches embed items as vectors and use dot-product self-attention to measure the relationship between items. We propose a novel textbfSTOchastic textbfSelf-textbfAttention(STOSA) to overcome these issues. We devise a novel Wasserstein Self-Attention module to characterize item-item position-wise relationships in sequences.
  arXiv  Detail & Related papers  (2022-01-16T12:38:45Z)
• TEA: A Sequential Recommendation Framework via Temporally Evolving Aggregations [12.626079984394766]
  We propose a novel sequential recommendation framework based on dynamic user-item heterogeneous graphs. We exploit the conditional random field to aggregate the heterogeneous graphs and user behaviors for probability estimation. We provide scalable and flexible implementations of the proposed framework.
  arXiv  Detail & Related papers  (2021-11-14T15:54:23Z)
• Contrastive Self-supervised Sequential Recommendation with Robust Augmentation [101.25762166231904]
  Sequential Recommendationdescribes a set of techniques to model dynamic user behavior in order to predict future interactions in sequential user data. Old and new issues remain, including data-sparsity and noisy data. We propose Contrastive Self-Supervised Learning for sequential Recommendation (CoSeRec)
  arXiv  Detail & Related papers  (2021-08-14T07:15:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.