# Towards Resolving Propensity Contradiction in Offline Recommender Learning

arXiv: https://arxiv.org/abs/1910.07295 · HTML: https://mlsanity.com/paper/1910.07295/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2019.

## Built on by (most cited first, 6 of 6)

- [Debiased Recommendation with Noisy Feedback](https://mlsanity.com/paper/2406.17182/) (2024)
- [Hyperparameter Optimization Can Even be Harmful in Off-Policy Learning and How to Deal with It](https://mlsanity.com/paper/2404.15084/) (2024)
- [Be Aware of the Neighborhood Effect: Modeling Selection Bias under Interference](https://mlsanity.com/paper/2404.19620/) (2024)
- [Addressing Correlated Latent Exogenous Variables in Debiased Recommender Systems](https://mlsanity.com/paper/2506.07517/) (2025)
- [BPL: Bias-adaptive Preference Distillation Learning for Recommender System](https://mlsanity.com/paper/2510.16076/) (2025)
- [DSL: Understanding and Improving Softmax Recommender Systems with Competition-Aware Scaling](https://mlsanity.com/paper/2602.07206/) (2026)

## Abstract (arXiv)

We study offline recommender learning from explicit rating feedback in the presence of selection bias. A current promising solution for the bias is the inverse propensity score (IPS) estimation. However, the performance of existing propensity-based methods can suffer significantly from the propensity estimation bias. In fact, most of the previous IPS-based methods require some amount of missing-completely-at-random (MCAR) data to accurately estimate the propensity. This leads to a critical self-contradiction; IPS is ineffective without MCAR data, even though it originally aims to learn recommenders from only missing-not-at-random feedback. To resolve this propensity contradiction, we derive a propensity-independent generalization error bound and propose a novel algorithm to minimize the theoretical bound via adversarial learning. Our theory and algorithm do not require a propensity estimation procedure, thereby leading to a well-performing rating predictor without the true propensity information. Extensive experiments demonstrate that the proposed approach is superior to a range of existing methods both in rating prediction and ranking metrics in practical settings without MCAR data.