# Exploring Longitudinal Effects of Session-based Recommendations

arXiv: https://arxiv.org/abs/2008.07226 · HTML: https://mlsanity.com/paper/2008.07226/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2020.

## Built on by (most cited first, 9 of 9)

- [A Survey on Popularity Bias in Recommender Systems](https://mlsanity.com/paper/2308.01118/) (2023)
- [Fair ranking: a critical review, challenges, and future directions](https://mlsanity.com/paper/2201.12662/) (2022)
- [Balancing Consumer and Business Value of Recommender Systems: A Simulation-based Analysis](https://mlsanity.com/paper/2203.05952/) (2022)
- [Understanding Longitudinal Dynamics of Recommender Systems with Agent-Based Modeling and Simulation](https://mlsanity.com/paper/2108.11068/) (2021)
- [Augmenting Netflix Search with In-Session Adapted Recommendations](https://mlsanity.com/paper/2206.02254/) (2022)
- [Debiasing the Cloze Task in Sequential Recommendation with Bidirectional Transformers](https://mlsanity.com/paper/2301.09210/) (2023)
- [Sequence Adaptation via Reinforcement Learning in Recommender Systems](https://mlsanity.com/paper/2108.01442/) (2021)
- [Multi-Objective Recommender Systems: Survey and Challenges](https://mlsanity.com/paper/2210.10309/) (2022)
- [Bias Mitigation for AI-Feedback Loops in Recommender Systems: A Systematic Literature Review and Taxonomy](https://mlsanity.com/paper/2509.00109/) (2025)

## Abstract (arXiv)

Session-based recommendation is a problem setting where the task of a recommender system is to make suitable item suggestions based only on a few observed user interactions in an ongoing session. The lack of long-term preference information about individual users in such settings usually results in a limited level of personalization, where a small set of popular items may be recommended to many users. This repeated exposure of such a subset of the items through the recommendations may in turn lead to a reinforcement effect over time, and to a system which is not able to help users discover new content anymore to the desirable extent. In this work, we investigate such potential longitudinal effects of session-based recommendations in a simulation-based approach. Specifically, we analyze to what extent algorithms of different types may lead to concentration effects over time. Our experiments in the music domain reveal that all investigated algorithms---both neural and heuristic ones---may lead to lower item coverage and to a higher concentration on a subset of the items. Additional simulation experiments however also indicate that relatively simple re-ranking strategies, e.g., by avoiding too many repeated recommendations in the music domain, may help to deal with this problem.