KDD论文推荐|在Airbnb中使用嵌入式做搜索排名来实现实时的个性化
国际知识发现与数据挖掘大会（ACM SIGKDD Conference on Knowledge Discovery and Data Mining,简称SIGKDD）是数据挖掘领域的顶级国际会议。
我们将持续对近年KDD的部分论文进行解读，目前已发出一期，内容如下：
KDD 2018共收到投稿论文1479篇，其中研究性论文983篇，应用数据科学论文496篇，均创下新高。
本文选取了KDD 2018最佳应用数据科学论文进行介绍：
论文题目
Real-time Personalization using Embeddings for Search Ranking at Airbnb
作者
Mihajlo Grbovic、Haibin Cheng
会议／年份
SIGKDD 2018
链接（点击阅读原文也可获取）
https://www.aminer.cn/archive/xiaoice-band-a-melody-and-arrangement-generation-framework-for-pop-music/5b67b45517c44aac1c8607e9
Abstract
Search Ranking and Recommendations are fundamental problems of crucial interest to major Internet companies, including web search engines, content publishing websites and marketplaces. However, despite sharing some common characteristics a one-size-fitsall solution does not exist in this space. Given a large difference in content that needs to be ranked, personalized and recommended, each marketplace has a somewhat unique challenge. Correspondingly, at Airbnb, a short-term rental marketplace, search and recommendation problems are quite unique, being a two-sided marketplace in which one needs to optimize for host and guest preferences, in a world where a user rarely consumes the same item twice and one listing can accept only one guest for a certain set of dates. In this paper we describe Listing and User Embedding techniques we developed and deployed for purposes of Real-time Personalization in Search Ranking and Similar Listing Recommendations, two channels that drive 99% of conversions. The embedding models were specifically tailored for Airbnb marketplace, and are able to capture guest’s short-term and long-term interests, delivering effective home listing recommendations. We conducted rigorous offline testing of the embedding models, followed by successful online tests before fully deploying them into production.
摘要
搜索排名是互联网公司主要关注的基本问题，包括网络搜索引擎、内容发布网站和市场。然而，尽管存在一些共同的特征，但在这个空间中不存在一个一刀切的解决方案。鉴于需要排名，个性化和推荐的内容存在巨大差异，每个市场都有一些独特的挑战。相应地，在Airbnb，一个短期租赁市场，搜索和推荐问题是非常独特的，是一个双面市场，人们需要优化主机和客人的偏好，在这个世界，用户很少消费相同的项目两次一个列表只能接受一个客人的特定日期。在本文中，我们描述了我们为搜索排名和类似列表建议中的实时个性化而开发和部署的列表和用户嵌入技术，这两个渠道可以驱动99％的转换。嵌入模型专为Airbnb市场量身定制，能够捕捉客人的短期和长期利益，提供有效的上市推荐。我们对嵌入模型进行了严格的离线测试，然后在将其完全部署到生产环境之前进行了成功的在线测试。
