A Simulation Framework for Studying Systemic Effects of Feedback Loops in Recommender Systems
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866912652887851008 |
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| author | Barlacchi, Gabriele Lalli, Margherita Ferragina, Emanuele Giannotti, Fosca Pappalardo, Luca |
| author_facet | Barlacchi, Gabriele Lalli, Margherita Ferragina, Emanuele Giannotti, Fosca Pappalardo, Luca |
| contents | Recommender systems continuously interact with users, creating feedback loops that shape both individual behavior and collective market dynamics. This paper introduces a simulation framework to model these loops in online retail environments, where recommenders are periodically retrained on evolving user-item interactions. Using the Amazon e-Commerce dataset, we analyze how different recommendation algorithms influence diversity, purchase concentration, and user homogenization over time. Results reveal a systematic trade-off: while the feedback loop increases individual diversity, it simultaneously reduces collective diversity and concentrates demand on a few popular items. Moreover, for some recommender systems, the feedback loop increases user homogenization over time, making user purchase profiles increasingly similar. These findings underscore the need for recommender designs that balance personalization with long-term diversity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_14857 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Simulation Framework for Studying Systemic Effects of Feedback Loops in Recommender Systems Barlacchi, Gabriele Lalli, Margherita Ferragina, Emanuele Giannotti, Fosca Pappalardo, Luca Information Retrieval Computers and Society Recommender systems continuously interact with users, creating feedback loops that shape both individual behavior and collective market dynamics. This paper introduces a simulation framework to model these loops in online retail environments, where recommenders are periodically retrained on evolving user-item interactions. Using the Amazon e-Commerce dataset, we analyze how different recommendation algorithms influence diversity, purchase concentration, and user homogenization over time. Results reveal a systematic trade-off: while the feedback loop increases individual diversity, it simultaneously reduces collective diversity and concentrates demand on a few popular items. Moreover, for some recommender systems, the feedback loop increases user homogenization over time, making user purchase profiles increasingly similar. These findings underscore the need for recommender designs that balance personalization with long-term diversity. |
| title | A Simulation Framework for Studying Systemic Effects of Feedback Loops in Recommender Systems |
| topic | Information Retrieval Computers and Society |
| url | https://arxiv.org/abs/2510.14857 |