学习用户偏好以激励共享经济中的探索
Learning User Preferences to Incentivize Exploration in the Sharing Economy
摘要：
我们研究了共享经济中的平台，并讨论了激励用户探索其他选择的必要性。例如，Airbnb等租赁平台通常依赖客户评论为用户提供有关不同选项的相关信息。然而，通常很大一部分选项没有任何可用的评论。这些备选办法往往被忽视为可行的选择，因而也不可能得到评价，造成了恶性循环。平台可以通过提供货币激励来吸引用户偏离他们的首选选项。为了有效地学习提供最佳的激励措施，我们考虑用户偏好的结构信息，并引入了一种新的算法-协调在线学习（CoOL）-学习与结构信息建模为凸约束。我们提供正式的保证，我们的算法的性能和测试的可行性，我们的方法在用户研究与数据的公寓在Airbnb。我们的研究结果表明，我们的方法非常适合学习适当的激励措施，并增加对调查平台的探索。
英文摘要：
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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