通过从宾客旅程中学习来个性化Airbnb搜索

By: Daochen Zha, Chun How Tan, Xin Liu, Bin Xu, Han Zhao, Xiaowei Liu, Jun Shi, Tracy Yu, Hui Gao, Huiji Gao, Liwei He, Michael Kinoti, Stephanie Moyerman, and Sanjeev Katariya

作者：Daochen Zha、Chun How Tan、Xin Liu、Bin Xu、Han Zhao、Xiaowei Liu、Jun Shi、Tracy Yu、Hui Gao、Huiji Gao、Liwei He、Michael Kinoti、Stephanie Moyerman以及Sanjeev Katariya

Introduction

引言

Planning a trip on Airbnb rarely happens in a single session. A guest searching for a place to stay in San Francisco might browse dozens of listings over several days, leaving behind a trail of views. Typically, over a period of years, that same guest will have accumulated many previous bookings, reviews, and the occasional cancellation. Taken together, these events reveal a great deal about what that guest values in a stay. For years, Airbnb’s search ranking captured this through hand-crafted features: aggregated statistics such as total past bookings or average listing price. These worked well, but as the feature count grew into the hundreds, the approach became harder to scale and increasingly limited in expressiveness. In this blog post, we describe how we built a sequence modeling system that encodes the full guest journey using a Transformer, learning richer representations of guest preferences to deliver more personalized search results.

在Airbnb上规划行程很少会在一次会话中完成。一位在旧金山寻找住处的房客可能会在几天内浏览数十个房源，留下一连串的浏览记录。通常情况下，在几年的时间里，同一位房客会积累许多过往的预订、评价以及偶尔的取消记录。综合来看，这些事件能揭示出该房客在住宿方面看重什么。多年来，Airbnb的搜索排名通过手工特征来捕捉这一点：例如过往总预订量或平均房源价格等聚合统计数据。这些方法效果很好，但随着特征数量增长到数百个，这种方法变得难以扩展，并且在表达能力上越来越受限。在这篇博客文章中，我们描述了如何构建一个序列建模系统，该系统使用Transformer对完整的房客旅程进行编码，学习更丰富的房客偏好表示，从而提供更具个性化的搜索结果。

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An example of a guest journey, which is typically long, exploratory, and complex.

房客旅程的一个示例，该旅程通常较长、具有探索性且复杂。

Challenges

挑战

Event sequences per guest present three core challenges. First, they are dominated by listing views, which account for the vast majority of all events — some guests accumulate hundreds of thousands of them — making raw sequences computat...