# Learning User Preferences to Incentivize Exploration in the Sharing Economy

arXiv: https://arxiv.org/abs/1711.08331 · HTML: https://mlsanity.com/paper/1711.08331/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2017.

## Built on by (most cited first, 4 of 4)

- [Incentivizing Exploration in Linear Bandits under Information Gap](https://mlsanity.com/paper/2104.03860/) (2021)
- [Initializing Services in Interactive ML Systems for Diverse Users](https://mlsanity.com/paper/2312.11846/) (2023)
- [Incentivized Exploration of Non-Stationary Stochastic Bandits](https://mlsanity.com/paper/2403.10819/) (2024)
- [Incentivized Lipschitz Bandits](https://mlsanity.com/paper/2508.19466/) (2025)

## Abstract (arXiv)

We study platforms in the sharing economy and discuss the need for incentivizing users to explore options that otherwise would not be chosen. For instance, rental platforms such as Airbnb typically rely on customer reviews to provide users with relevant information about different options. Yet, often a large fraction of options does not have any reviews available. Such options are frequently neglected as viable choices, and in turn are unlikely to be evaluated, creating a vicious cycle. Platforms can engage users to deviate from their preferred choice by offering monetary incentives for choosing a different option instead. To efficiently learn the optimal incentives to offer, we consider structural information in user preferences and introduce a novel algorithm - Coordinated Online Learning (CoOL) - for learning with structural information modeled as convex constraints. We provide formal guarantees on the performance of our algorithm and test the viability of our approach in a user study with data of apartments on Airbnb. Our findings suggest that our approach is well-suited to learn appropriate incentives and increase exploration on the investigated platform.