@inproceedings{
greenwood2024useritem,
title={User-item fairness tradeoffs in recommendations},
author={Sophie Greenwood and Sudalakshmee Chiniah and Nikhil Garg},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ZOZjMs3JTs}
}
In the basic recommendation paradigm, the most (predicted) relevant item is recommended to each user. This may result in some items receiving lower exposure than they "should"; to counter this, several algorithmic approaches have been developed to ensure *item fairness*. These approaches necessarily degrade recommendations for some users to improve outcomes for items, leading to *user fairness* concerns. In turn, a recent line of work has focused on developing algorithms for multi-sided fairness, to jointly optimize user fairness, item fairness, and overall recommendation quality. This induces the question: *what is the tradeoff between these objectives, and what are the characteristics of (multi-objective) optimal solutions?* Theoretically, we develop a model of recommendations with user and item fairness objectives and characterize the solutions of fairness-constrained optimization. We identify two phenomena: (a) when user preferences are diverse, there is "free" item and user fairness; and (b) users whose preferences are misestimated can be *especially* disadvantaged by item fairness constraints. Empirically, we prototype a recommendation system for preprints on arXiv and implement our framework, measuring the phenomena in practice and showing how these phenomena inform the *design* of markets with recommendation systems-intermediated matching.
User-item fairness tradeoffs in recommendations
Sophie Greenwood Sudalakshmee Chiniah Nikhil Garg