How Recommendation System Feedback Loops Disproportionately Hurt Users with Minority Preferences
Algorithmic recommendation systems impact the choices of millions of consumers daily; these systems exist for a wide variety of markets, including both consumable and durable goods, as well as digital and physical goods. After a recommendation system is in place, it will need to be periodically updated to incorporate new users, new items, and new observed interactions between users and items. These observed data, however, are algorithmically confounded: they are the result of a feedback loop between human choices and the existing algorithmic recommendation system. Using simulations, we explore the impact of updating a recommendation system. We find that the choices surrounding system updates have the greatest impact on users belonging to minority preference segments.
Allison Chaney is an Assistant Professor of Marketing in the Fuqua School of Business at Duke University. Her research is at the intersection of machine learning and marketing, focusing on developing scalable and interpretable machine learning methods and understanding the impacts of these methods on individuals and society when they are deployed in real-world markets. She received her Ph.D. in Computer Science at Princeton University and holds a B.A. in Computer Science and a B.S. in Engineering from Swarthmore College. She has worked for Pixar Animation Studios and the Yorba Foundation for open-source software and has collaborated with many research teams in industry including eBay/Hunch, Etsy, and Microsoft Research.