Airbnb is a two-sided marketplace, bringing together hosts who own listings for rent, with prospective guests from around the globe. Applying neural network-based learning to rank techniques has led to significant improvements in matching guests with hosts. These improvements in ranking were driven by a core strategy: order the listings by their estimated booking probabilities, then iterate on techniques to make these booking probability estimates more and more accurate. Embedded implicitly in this strategy was an assumption that the booking probability of a listing could be determined independently of other listings in search results. In this paper we discuss how this assumption, pervasive throughout the commonly-used learning to rank frameworks, is false. We provide a theoretical foundation correcting this assumption, followed by efficient neural network architectures based on the theory. Explicitly accounting for possible similarities between listings, and reducing them to diversify the search results generated strong positive impact. We discuss these metric wins as part of the online A/B tests of the theory. Our method provides a practical way to diversify search results for large-scale production ranking systems.
翻译:Airbnb 是一个双面市场, 将拥有租房名单的东道主和来自全球各地的预期客人聚集在一起。 应用神经网络学习和排名技术导致在将客人与东道主匹配方面有了显著的改进。 排序的这些改进是由核心战略驱动的: 按其估计的预订概率排列列表, 然后反复使用技术使这些订票概率估计越来越准确。 这个战略隐含地假定, 订票列表的概率可以独立于其他搜索结果列表。 在本文中, 我们讨论这个在常用的学习框架排行榜中普遍存在的假设如何是虚假的。 我们提供了一个理论基础, 纠正这一假设, 并随后根据理论建立高效的神经网络结构。 明确计算列表之间的可能相似之处, 并减少它们使其多样化搜索结果产生巨大的积极影响。 我们将这些量化结果作为理论的在线 A/ B 测试的一部分来讨论。 我们的方法为大规模生产排名系统提供了多样化搜索结果的实用方法。