# It Is Different When Items Are Older: Debiasing Recommendations When Selection Bias and User Preferences Are Dynamic

arXiv: https://arxiv.org/abs/2111.12481 · HTML: https://mlsanity.com/paper/2111.12481/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2021.

## Built on by (most cited first, 10 of 10)

- [A Survey on Popularity Bias in Recommender Systems](https://mlsanity.com/paper/2308.01118/) (2023)
- [A Survey on Fairness-aware Recommender Systems](https://mlsanity.com/paper/2306.00403/) (2023)
- [Recent Developments in Recommender Systems: A Survey](https://mlsanity.com/paper/2306.12680/) (2023)
- [Towards Disentangling Relevance and Bias in Unbiased Learning to Rank](https://mlsanity.com/paper/2212.13937/) (2022)
- [RecFusion: A Binomial Diffusion Process for 1D Data for Recommendation](https://mlsanity.com/paper/2306.08947/) (2023)
- [A Lightweight Method for Modeling Confidence in Recommendations with Learned Beta Distributions](https://mlsanity.com/paper/2308.03186/) (2023)
- [Be Aware of the Neighborhood Effect: Modeling Selection Bias under Interference](https://mlsanity.com/paper/2404.19620/) (2024)
- [Finding Interest Needle in Popularity Haystack: Improving Retrieval by Modeling Item Exposure](https://mlsanity.com/paper/2503.23630/) (2025)
- [Recover Experimental Data with Selection Bias using Counterfactual Logic](https://mlsanity.com/paper/2506.00335/) (2025)
- [Debiasing Sequential Recommendation with Time-aware Inverse Propensity Scoring](https://mlsanity.com/paper/2603.04986/) (2026)

## Abstract (arXiv)

User interactions with recommender systems (RSs) are affected by user selection bias, e.g., users are more likely to rate popular items (popularity bias) or items that they expect to enjoy beforehand (positivity bias). Methods exist for mitigating the effects of selection bias in user ratings on the evaluation and optimization of RSs. However, these methods treat selection bias as static, despite the fact that the popularity of an item may change drastically over time and the fact that user preferences may also change over time. We focus on the age of an item and its effect on selection bias and user preferences. Our experimental analysis reveals that the rating behavior of users on the MovieLens dataset is better captured by methods that consider effects from the age of item on bias and preferences. We theoretically show that in a dynamic scenario in which both the selection bias and user preferences are dynamic, existing debiasing methods are no longer unbiased. To address this limitation, we introduce DebiAsing in the dyNamiC scEnaRio (DANCER), a novel debiasing method that extends the inverse propensity scoring debiasing method to account for dynamic selection bias and user preferences. Our experimental results indicate that DANCER improves rating prediction performance compared to debiasing methods that incorrectly assume that selection bias is static in a dynamic scenario. To the best of our knowledge, DANCER is the first debiasing method that accounts for dynamic selection bias and user preferences in RSs.