  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.
