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  Learning User Preferences to Incentivize Exploration in the Sharing Economy

Hirnschall, C., Singla, A., Tschiatschek, S., & Krause, A. (2017). Learning User Preferences to Incentivize Exploration in the Sharing Economy. Retrieved from http://arxiv.org/abs/1711.08331.

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arXiv:1711.08331.pdf (Preprint), 2MB
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arXiv:1711.08331.pdf
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File downloaded from arXiv at 2018-03-26 12:41 Longer version of AAAI'18 paper. arXiv admin note: text overlap with arXiv:1702.02849
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 Urheber:
Hirnschall, Christoph1, Autor
Singla, Adish2, Autor                 
Tschiatschek, Sebastian1, Autor
Krause, Andreas1, Autor
Affiliations:
1External Organizations, ou_persistent22              
2Group A. Singla, Max Planck Institute for Software Systems, Max Planck Society, ou_2541698              

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Schlagwörter: Computer Science, Learning, cs.LG,cs.SI,Statistics, Machine Learning, stat.ML
 Zusammenfassung: 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.

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Sprache(n): eng - English
 Datum: 2017-11-172017-11-242017
 Publikationsstatus: Online veröffentlicht
 Seiten: 18 p.
 Ort, Verlag, Ausgabe: -
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 Identifikatoren: arXiv: 1711.08331
URI: http://arxiv.org/abs/1711.08331
BibTex Citekey: Hirnschall_arXiv1711.08331
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