模拟推荐系统反馈循环，揭示个性化与集体多样性间的权衡
A Simulation Framework for Studying Systemic Effects of Feedback Loops in Recommender Systems
- 构建在线零售场景的仿真框架，周期性重训练推荐模型
- 反馈环使个体多样性上升，但集体多样性下降，热门商品更集中
- 部分系统导致用户购买行为趋同，适合关注推荐系统长期影响的研究者
推荐系统持续与用户互动，形成反馈循环，影响个体行为与整体市场动态。本文提出一种仿真框架，模拟在线零售环境中推荐系统在不断演变的用户-物品交互数据上周期性重训练的情形。基于亚马逊电商数据集，分析不同推荐算法对多样性、购买集中度及用户同质化的长期影响。结果显示：反馈循环虽提升个体多样性，却同时降低集体多样性，使需求集中在少数热门商品；某些推荐系统还导致用户购买模式趋于相似。研究强调需在个性化与长期多样性之间寻求平衡。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。