# Algorithmic Content Selection and the Impact of User Disengagement

arXiv: https://arxiv.org/abs/2410.13108 · HTML: https://mlsanity.com/paper/2410.13108/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 40 references that are in the map.
- First posted on arXiv 17 Oct 2024.

## Builds on (most cited first, 10 of 10)

- [Explore no more: Improved high-probability regret bounds for non-stochastic bandits](https://mlsanity.com/paper/1506.03271/) (2015)
- [Stochastic Bandits with Linear Constraints](https://mlsanity.com/paper/2006.10185/) (2020)
- [The Challenge of Understanding What Users Want: Inconsistent Preferences and Engagement Optimization](https://mlsanity.com/paper/2202.11776/) (2022)
- [Rebounding Bandits for Modeling Satiation Effects](https://mlsanity.com/paper/2011.06741/) (2020)
- [Clickbait vs. Quality: How Engagement-Based Optimization Shapes the Content Landscape in Online Platforms](https://mlsanity.com/paper/2401.09804/) (2024)
- [Modeling Attrition in Recommender Systems with Departing Bandits](https://mlsanity.com/paper/2203.13423/) (2022)
- [Fatigue-aware Bandits for Dependent Click Models](https://mlsanity.com/paper/2008.09733/) (2020)
- [A Last Switch Dependent Analysis of Satiation and Seasonality in Bandits](https://mlsanity.com/paper/2110.11819/) (2021)
- [Efficient Algorithms for Planning with Participation Constraints](https://mlsanity.com/paper/2205.07767/) (2022)
- [Exploration, Exploitation, and Engagement in Multi-Armed Bandits with Abandonment](https://mlsanity.com/paper/2205.13566/) (2022)

## Abstract (arXiv)

Digital services face a fundamental trade-off in content selection: they must balance the immediate revenue gained from high-reward content against the long-term benefits of maintaining user engagement. Traditional multi-armed bandit models assume that users remain perpetually engaged, failing to capture the possibility that users may disengage when dissatisfied, thereby reducing future revenue potential. In this work, we introduce a model for the content selection problem that explicitly accounts for variable user engagement and disengagement. In our framework, content that maximizes immediate reward is not necessarily optimal in terms of fostering sustained user engagement. Our contributions are twofold. First, we develop computational and statistical methods for offline optimization and online learning of content selection policies. For users whose engagement patterns are defined by k distinct levels, we design a dynamic programming algorithm that computes the exact optimal policy in O(k2) time. Moreover, we derive no-regret learning guarantees for an online learning setting in which the platform serves a series of users with unknown and potentially adversarial engagement patterns. Second, we introduce the concept of modified demand elasticity which captures how small changes in a user's overall satisfaction affect the platform's ability to secure long-term revenue. This notion generalizes classical demand elasticity by incorporating the dynamics of user re-engagement, thereby revealing key insights into the interplay between engagement and revenue. Notably, our analysis uncovers a counterintuitive phenomenon: although higher friction (i.e., a reduced likelihood of re-engagement) typically lowers overall revenue, it can simultaneously lead to higher user engagement under optimal content selection policies.