🤖 AI Summary
This paper investigates the long-term impact of feedback loops in recommender systems on individual behavior and collective market dynamics, focusing on the tension between personalization and diversity in online retail. We propose a reproducible simulation framework grounded in real-world Amazon e-commerce data, wherein multiple recommendation algorithms are periodically retrained to model sustained user–system interaction over time. Our analysis reveals a pervasive paradox: while individual-level diversity—measured by apparent interest divergence—increases, collective-level homogenization intensifies, as actual purchase behavior converges toward popular items, exacerbating item popularity skew and significantly eroding market-level diversity. The core contribution is the first systematic identification and quantification of a structural trade-off between individual and collective diversity, providing both theoretical grounding and empirical benchmarks for designing bias-mitigating, sustainable recommender systems.
📝 Abstract
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.