Envious Explore and Exploit
Omer Ben-Porat, Yotam Gafni, Or Markovetzki

TL;DR
This paper investigates the societal impact of explore-and-exploit strategies in recommendation systems, focusing on envy among users and proposing methods to balance fairness and efficiency.
Contribution
It introduces a model analyzing envy in recommendation systems, provides bounds on envy under various arrival mechanisms, and proposes an algorithm balancing fairness and welfare.
Findings
Tight envy bounds for uniform arrival mechanisms
Upper bounds on envy for nudged arrival mechanisms
An algorithm achieving constant envy with near-optimal welfare
Abstract
Explore-and-exploit tradeoffs play a key role in recommendation systems (RSs), aiming at serving users better by learning from previous interactions. Despite their commercial success, the societal effects of explore-and-exploit mechanisms are not well understood, especially regarding the utility discrepancy they generate between different users. In this work, we measure such discrepancy using the economic notion of envy. We present a multi-armed bandit-like model in which every round consists of several sessions, and rewards are realized once per round. We call the latter property reward consistency, and show that the RS can leverage this property for better societal outcomes. On the downside, doing so also generates envy, as late-to-arrive users enjoy the information gathered by early-to-arrive users. We examine the generated envy under several arrival order mechanisms and virtually…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsCybercrime and Law Enforcement Studies
