论文标题
改善Airbnb搜索的深度学习
Improving Deep Learning For Airbnb Search
论文作者
论文摘要
深度学习在搜索排名中的应用是Airbnb最有影响力的产品改进之一。但是，在您推出深度学习模型后接下来会发生什么？在本文中，我们描述了超越的旅程，讨论了我们所说的改进搜索的ABC：A架构，b for Bias for Bias和C以进行冷启动。对于体系结构，我们描述了一个新的排名神经网络，重点是在完全连接的两个层网络之外演变的流程。在处理排名中的位置偏见时，我们描述了一种新颖的方法，该方法导致了解决DNN历史上发现具有挑战性的库存方面最重大的改进之一。为了解决寒冷的开始，我们描述了我们对改善平台上新列表处理的问题和变化的看法。我们希望将过渡到深度学习的团队排名将发现这是一个实用的案例研究，以了解如何在DNN上迭代。
The application of deep learning to search ranking was one of the most impactful product improvements at Airbnb. But what comes next after you launch a deep learning model? In this paper we describe the journey beyond, discussing what we refer to as the ABCs of improving search: A for architecture, B for bias and C for cold start. For architecture, we describe a new ranking neural network, focusing on the process that evolved our existing DNN beyond a fully connected two layer network. On handling positional bias in ranking, we describe a novel approach that led to one of the most significant improvements in tackling inventory that the DNN historically found challenging. To solve cold start, we describe our perspective on the problem and changes we made to improve the treatment of new listings on the platform. We hope ranking teams transitioning to deep learning will find this a practical case study of how to iterate on DNNs.
