Incentive-Aware Recommender Systems in Two-Sided Markets

TL;DR

This paper introduces an incentive-aware recommender system for two-sided online markets that aligns agent incentives with optimal exploration, ensuring asymptotic performance and fairness in repeated interactions.

Contribution

It develops novel algorithms for incentive-compatible recommendations in two-sided markets, handling known and unknown opportunity costs, with proven fairness and optimality.

Findings

Algorithms achieve asymptotic optimal regret.

Proposed methods ensure ex-post fairness.

Framework effectively balances exploration and exploitation.

Abstract

Online platforms in the Internet Economy commonly incorporate recommender systems that recommend products (or "arms") to users (or "agents"). A key challenge in this domain arises from myopic agents who are naturally incentivized to exploit by choosing the optimal arm based on current information, rather than exploring various alternatives to gather information that benefits the collective. We propose a novel recommender system that aligns with agents' incentives while achieving asymptotically optimal performance, as measured by regret in repeated interactions. Our framework models this incentive-aware system as a multi-agent bandit problem in two-sided markets, where the interactions of agents and arms are facilitated by recommender systems on online platforms. This model incorporates incentive constraints induced by agents' opportunity costs. In scenarios where opportunity costs are known to the platform, we show the existence of an incentive-compatible recommendation algorithm. This algorithm pools recommendations between a genuinely good arm and an unknown arm using a randomized and adaptive strategy. Moreover, when these opportunity costs are unknown, we introduce an algorithm that randomly pools recommendations across all arms, utilizing the cumulative loss from each arm as feedback for strategic exploration. We demonstrate that both algorithms satisfy an ex-post fairness criterion, which protects agents from over-exploitation. All code for using the proposed algorithms and reproducing results is made available on GitHub.