![[2602.16315] The Diversity Paradox revisited: Systemic Effects of Feedback Loops in Recommender Systems](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.16315] The Diversity Paradox revisited: Systemic Effects of Feedback Loops in Recommender Systems
Summary
This paper revisits the diversity paradox in recommender systems, exploring how feedback loops influence user behavior and consumption patterns over time.
Why It Matters
Understanding the systemic effects of feedback loops in recommender systems is crucial for improving algorithm design. The findings challenge static evaluations and highlight the need for dynamic models that reflect real-world user interactions, impacting areas like online retail and music streaming.
Key Takeaways
- Feedback loops in recommender systems can lead to increased individual consumption diversity.
- Collective demand may become concentrated, amplifying popular items.
- Static evaluations of diversity can be misleading; individual diversity often decreases over time.
- Dynamic modeling is essential for accurate assessments of recommender system performance.
- The study emphasizes the importance of considering feedback-loop dynamics in algorithm design.
Computer Science > Information Retrieval arXiv:2602.16315 (cs) [Submitted on 18 Feb 2026] Title:The Diversity Paradox revisited: Systemic Effects of Feedback Loops in Recommender Systems Authors:Gabriele Barlacchi, Margherita Lalli, Emanuele Ferragina, Fosca Giannotti, Dino Pedreschi, Luca Pappalardo View a PDF of the paper titled The Diversity Paradox revisited: Systemic Effects of Feedback Loops in Recommender Systems, by Gabriele Barlacchi and 4 other authors View PDF HTML (experimental) Abstract:Recommender systems shape individual choices through feedback loops in which user behavior and algorithmic recommendations coevolve over time. The systemic effects of these loops remain poorly understood, in part due to unrealistic assumptions in existing simulation studies. We propose a feedback-loop model that captures implicit feedback, periodic retraining, probabilistic adoption of recommendations, and heterogeneous recommender systems. We apply the framework on online retail and music streaming data and analyze systemic effects of the feedback loop. We find that increasing recommender adoption may lead to a progressive diversification of individual consumption, while collective demand is redistributed in model- and domain-dependent ways, often amplifying popularity concentration. Temporal analyses further reveal that apparent increases in individual diversity observed in static evaluations are illusory: when adoption is fixed and time unfolds, individual diversity consiste...
