学习意图为Airbnb搜索预订指标

B. Turnbull
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引用次数: 7

摘要

Airbnb是一个双边租赁市场,提供各种独特和更传统的住宿选择。与其他在线市场类似,我们投资于优化搜索UI上的内容和排名相关性,以改善客人的在线搜索体验。然而,独特的Airbnb库存暴露了一些重大的数据挑战。考虑到预订不太传统的住宿的高风险,用户在做出预订决定之前,可能会花费数天到数周的时间搜索和浏览许多住宿“列表”的描述页面。此外,关于列表的许多信息是非结构化的,只有在用户浏览列表页面上的详细信息后才能找到。因此,我们发现传统的搜索指标在我们的平台上并不适用。单个用户行为的基本指标,如点击率、浏览列表的数量或停留时间,与我们的下游业务指标并不一致。为了解决这些问题,我们利用机器学习从丰富的行为数据中分离出意图信号。这些信号具有关键的应用,包括分析见解、建模输入排序和实验速度。在本文中,我们描述了一个基于模型的用户意图度量的发展,“有意上市视图”,它结合了上市描述页面上各种用户微动作的信号。我们证明了这种学习的度量与下游转换度量方向相关,并且在各种历史搜索实验中都很敏感。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Learning Intent to Book Metrics for Airbnb Search
Airbnb is a two-sided rental marketplace offering a variety of unique and more traditional accommodation options. Similar to other online marketplaces we invest in optimizing the content surfaced on the search UI and ranking relevance to improve the guest online search experience. The unique Airbnb inventory, however, surfaces some major data challenges. Given the high stakes of booking less traditional accommodations, users can spend many days to weeks searching and scanning the description page of many accommodation ”listings” before making a decision to book. Moreover, much of the information about a listing is unstructured and can only be found by the user after they go through the details on the listing page. As a result, we have found traditional search metrics do not work well in the context of our platform. Basic metrics of single user actions, such as click-through-rates, number of listings viewed, or dwell time, are not consistently directionally correlated with our downstream business metrics. To address these issues we leverage machine learning to isolate signals of intent from rich behavioral data. These signals have key applications including analytical insights, ranking modeling inputs, and experimentation velocity. In this paper, we describe the development of a model-based user intent metric, ”intentful listing view”, which combines the signals of a variety of user micro-actions on the listing description page. We demonstrate this learned metric is directionally correlated with downstream conversion metrics and sensitive across a variety of historical search experiments.
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