Proceedings ArticleDOI
Customized Regression Model for Airbnb Dynamic Pricing
Peng Ye,Julian Qian,Jieying Chen,Chen-hung Wu,Yitong Zhou,Spencer de Mars,Frank Yang,Li Zhang +7 more
- pp 932-940
TLDR
The proposed pricing strategy has been deployed in production to power the Price Tips and Smart Pricing tool on Airbnb and online A/B testing results demonstrate the effectiveness of the proposed strategy model.Abstract:
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.read more
Citations
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Journal ArticleDOI
Feature-Based Dynamic Pricing
TL;DR: This work considers the problem faced by a firm that receives highly differentiated products in an online fashion and needs to price these products to sell them to its customer base.
Journal ArticleDOI
Feature-Based Dynamic Pricing
TL;DR: This work considers the problem faced by a firm that receives highly differentiated products in an online fashion and needs to price them in order to sell them to its customer base, and proposes a modification of the prior algorithm where uncertainty sets are replaced by their Lowner-John ellipsoids.
Proceedings ArticleDOI
Applying Deep Learning to Airbnb Search
Malay Haldar,Mustafa Abdool,Prashant Ramanathan,Tao Xu,Shulin Yang,Huizhong Duan,Qing Zhang,Nick Barrow-Williams,Bradley C. Turnbull,Brendan M. Collins,Thomas Legrand +10 more
TL;DR: This paper discusses the work done in applying neural networks in an attempt to break out of a plateau, and presents a story of the elements the authors found useful in applying Neural networks to a real life product.
Journal ArticleDOI
Price Discrimination with Fairness Constraints
TL;DR: In this article, the authors consider the problem of setting prices for different groups under fairness constraints and propose four definitions: fairness in price, demand, consumer surplus, and no-purchase valuation.
Proceedings ArticleDOI
Applying Deep Learning To Airbnb Search
Malay Haldar,Mustafa Abdool,Prashant Ramanathan,Tao Xu,Shulin Yang,Huizhong Duan,Qing Zhang,Nick Barrow-Williams,Bradley C. Turnbull,Brendan M. Collins,Thomas Legrand +10 more
TL;DR: In this article, the authors discuss the work done in applying neural networks in an attempt to break out of the plateau of a gradient boosted decision tree model and present their perspective not with the intention of pushing the frontier of new modeling techniques.
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