150 Successful Machine Learning Models: 6 Lessons Learned at Booking.com
150 Successful Machine Learning Models: 6 Lessons Learned at Booking.com
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150 个成功的机器学习模型：从 Booking.com 学到的 6 个经验教训
DOI：
10.1145/3292500.3330744
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
PabloA . Estevez
中科院分区：
文献类型：
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作者：
Lucas Bernardi;Themistoklis Mavridis;PabloA . Estevez
Booking.com是世界上最大的在线旅行社，数百万客人在这里找到他们的住宿，数百万住宿提供商列出他们的财产，包括酒店，公寓，床和早餐，招待所等。在过去的几年里，我们应用机器学习来改善客户和业务的体验。虽然大多数机器学习文献都集中在该领域的算法或数学方面，但关于机器学习如何在商业利益至关重要的工业环境中产生有意义的影响的文章并不多。我们对大约150个成功的面向客户的机器学习应用程序进行了分析，这些应用程序由Booking.com上的数十个团队开发，面向全球数亿用户，并通过严格的随机对照试验进行了验证。在机器学习项目的各个阶段之后，我们描述了我们的方法，我们发现的许多挑战，以及我们在整个组织中扩展这种复杂技术时学到的经验教训。我们的主要结论是，一个迭代的、假设驱动的过程，与其他学科相结合，是构建150个成功的机器学习产品的基础。
Booking.com is the world's largest online travel agent where millions of guests find their accommodation and millions of accommodation providers list their properties including hotels, apartments, bed and breakfasts, guest houses, and more. During the last years we have applied Machine Learning to improve the experience of our customers and our business. While most of the Machine Learning literature focuses on the algorithmic or mathematical aspects of the field, not much has been published about how Machine Learning can deliver meaningful impact in an industrial environment where commercial gains are paramount. We conducted an analysis on about 150 successful customer facing applications of Machine Learning, developed by dozens of teams in Booking.com, exposed to hundreds of millions of users worldwide and validated through rigorous Randomized Controlled Trials. Following the phases of a Machine Learning project we describe our approach, the many challenges we found, and the lessons we learned while scaling up such a complex technology across our organization. Our main conclusion is that an iterative, hypothesis driven process, integrated with other disciplines was fundamental to build 150 successful products enabled by Machine Learning.