Learning User Preferences to Incentivize Exploration in the Sharing Economy

TL;DR

This paper introduces a novel algorithm called Coordinated Online Learning (CoOL) that learns optimal monetary incentives to encourage users to explore less-known options in sharing economy platforms, thereby increasing information diversity.

Contribution

The paper proposes a new algorithm that leverages structural information in user preferences to efficiently learn incentives, with formal guarantees and practical validation.

Findings

The CoOL algorithm effectively learns incentives to promote exploration.

In a user study, the approach increased exploration of less-reviewed options.

The method outperforms baseline strategies in incentivizing user exploration.

Abstract

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.