图 书 馆
Airbnb动态定价的自定义回归模型Customized Regression Model for Airbnb Dynamic Pricing |
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| 课程网址: | http://videolectures.net/kdd2018_ye_airbnb_dynamic_pricing/ |
| 主讲教师: | Peng Ye |
| 开课单位: | Airbnb |
| 开课时间: | 2018-11-23 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | This paper describes the pricing strategy model deployed at Airbnb, an online marketplace for sharing home and experience. The goal of price optimization is to help hosts who share their homes on Airbnb set the optimal price for their listings. In contrast to conventional pricing problems, where pricing strategies are applied to a large quantity of identical products, there are no “identical” products on Airbnb, because each listing on our platform offers unique values and experiences to our guests. The unique nature of Airbnb listings makes it very difficult to estimate an accurate demand curve that’s required to apply conventional revenue maximization pricing strategies. Our pricing system consists of three components. First, a binary classification model predicts the booking probability of each listing-night. Second, a regression model predicts the optimal price for each listing-night, in which a customized loss function is used to guide the learning. Finally, we apply additional personalization logic on top of the output from the second model to generate the final price suggestions. In this paper, we focus on describing the regression model in the second stage of our pricing system. We also describe a novel set of metrics for offline evaluation. The proposed pricing strategy has been deployed in production to power the Price Tips and Smart Pricing tool on Airbnb. Online A/B testing results demonstrate the effectiveness of the proposed strategy model. |
| 关 键 词: | 定价策略; 独特特性; 需求曲线 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2022-11-02：chenjy |
| 最后编审: | 2022-11-02：chenjy |
| 阅读次数: | 47 |