arXiv cs.CY· Guangyuan Weng, Lin Chen, Ciro Beneduce, Giovanni Mauro, Massimiliano Luca, Takahiro Yabe, Bruno Lepri, Luca Pappalardo, Esteban Moro·· 11 小时前AI 评分48
研究：推荐系统反馈循环加剧城市收入隔离与场所消失
Recommendation Feedback Loops Shape Urban Income Segregation and Venue Visitation
AI 导读
基于美国四大都市区细粒度出行数据的长时模拟发现，在各类推荐算法下至少五分之一餐饮场所不再有模拟居民到访，其中过半损失源于模型重训练。算法越准确，人群隔离反而越严重，最准确的算法使实际隔离程度高出 15-21%。在平台现有算法上叠加偏好冷门场所的重排序干预，可同时降低场所消失与隔离，且基本不会把人引向不适的场所。
正文 · 原文
Abstract:Location-based recommender systems shape where people go in cities and learn from the visits they steer, forming a human-AI feedback loop. This feedback loop shapes whom people share places with across income groups and which venues keep attracting customers. Yet platforms judge these systems by their offline accuracy on past visits, a test in which the suggestions never change where people go and which therefore cannot capture long-term effects. Studying these effects in cities is challenging because, unlike online resources, travel constraints and limited venue capacity shape where people can go, while each recommended visit also changes whom other visitors encounter. To capture such features, we build a long-term simulation framework based on fine-grained human mobility data from four U.S. metropolitan areas, with distance and capacity constraints. Focusing on food venues, we study two urban consequences: how unevenly people's exposure is distributed across income groups (experienced income segregation) and the share of venues receiving no visits from simulated residents (venue disappearance). We find that, under every algorithm and in every metropolitan area, at least a fifth of venues stop receiving visits, and that more than half of this loss arises from retraining. More importantly, more accurate algorithms leave people more segregated, and the most accurate algorithm leaves people 15-21% more segregated than they actually are. We then test a re-ranking intervention that favours less popular venues and runs on top of a platform's existing algorithm. It lowers both venue disappearance and segregation across algorithms, largely without directing people to venues where they may feel less comfortable. Our results suggest that impact assessments of recommender systems should account for the feedback loop and report segregation and venue disappearance beyond accuracy.
| Comments: | 13 pages, 5 figures, 7 tables. Code: this https URL |
| Subjects: | Information Retrieval (cs.IR); Computers and Society (cs.CY); Social and Information Networks (cs.SI); Physics and Society (physics.soc-ph) |
| ACM classes: | H.3.3; J.4 |
| Cite as: | arXiv:2610.41911 [cs.IR] |
| (or arXiv:2610.41911v1 [cs.IR] for this version) | |
| https://doi.org/10.48550/arXiv.2610.41911 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Guangyuan Weng [view email]
[v1]
Fri, 30 Oct 2026 15:47:06 UTC (479 KB)
来源：arXiv cs.CY · arxiv.org