# Exploring Popularity Bias in Session-based Recommendation

arXiv: https://arxiv.org/abs/2312.07855 · HTML: https://mlsanity.com/paper/2312.07855/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 12 references that are in the map.
- First posted on arXiv 13 Dec 2023.

## Builds on (most cited first, 8 of 8)

- [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015)
- [Session-based Recommendation with Graph Neural Networks](https://mlsanity.com/paper/1811.00855/) (2018)
- [Recommendations as Treatments: Debiasing Learning and Evaluation](https://mlsanity.com/paper/1602.05352/) (2016)
- [Neural Attentive Session-based Recommendation](https://mlsanity.com/paper/1711.04725/) (2017)
- [Recurrent Neural Networks with Top-k Gains for Session-based Recommendations](https://mlsanity.com/paper/1706.03847/) (2017)
- [Evaluation of Session-based Recommendation Algorithms](https://mlsanity.com/paper/1803.09587/) (2018)
- [Empirical Analysis of Session-Based Recommendation Algorithms](https://mlsanity.com/paper/1910.12781/) (2019)
- [The Simpson's Paradox in the Offline Evaluation of Recommendation Systems](https://mlsanity.com/paper/2104.08912/) (2021)

## Abstract (arXiv)

Existing work has revealed that large-scale offline evaluation of recommender systems for user-item interactions is prone to bias caused by the deployed system itself, as a form of closed loop feedback. Many adopt the textitpropensity concept to analyze or mitigate this empirical issue. In this work, we extend the analysis to session-based setup and adapted propensity calculation to the unique characteristics of session-based recommendation tasks. Our experiments incorporate neural models and KNN-based models, and cover both the music and the e-commerce domain. We study the distributions of propensity and different stratification techniques on different datasets and find that propensity-related traits are actually dataset-specific. We then leverage the effect of stratification and achieve promising results compared to the original models.