Customized Regression Model for Airbnb Dynamic Pricing

Ye, Peng; Qian, Julian; Chen, Jieying; Wu, Chen-hung; Zhou, Yi; Mars, Spencer De; Yang, Fan; Zhang, Li

doi:10.1145/3219819.3219830

Cited by 63 publications

(47 citation statements)

References 15 publications

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“…In this section, we present a customized loss function that takes into account a regret of pricing low, conditional on the ancillary being purchased; and a penalty for recommending high, conditional on it not being purchased. The objective function is inspired from the strategic model proposed by Ye et al [21] and ϵ-insensitive loss used in SVR [17]. We enhance this strategic model using latent variables to incorporate the monotonicity in the willingness to pay assumption in our loss function.…”

Section: Customized Loss Function For Dnn-clmentioning

confidence: 99%

“…In this section, we define the metrics that we use to serve as guides through hyper-parameter tuning and to ensure that nightly update of DNN weights do not overfit the data. We use the Price Decrease Recall (PDR) and Price Decrease Precision (PDP) scores presented by Ye et al [21] due to their high correlation with the airline's business metrics. PDR measures how likely our recommended prices are lower than the current offered prices for non purchased ancillary and PDP measures the percentage for recommended prices that are lower than current offered prices for non purchased ancillary.…”

Section: Offline Metricsmentioning

confidence: 99%

“…We used the offline metrics defined in Section 4.1 to perform the comparison between the two-stage sequential forecasting and optimization models (APP-LM and APP-DES), and the simultaneous end-to-end pricing model (DNN-CL). Given the business requirement to provide discounts on the human-recommended price, we considered Regret Score (RS) and Price Decrease Recall (PDR) as more important than PDP and PDF1 [21]. Our results are summarized in Table 4.…”

Section: Offline Experimentsmentioning

confidence: 99%

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Dynamic Pricing for Airline Ancillaries with Customer Context

Shukla

Kolbeinsson

Otwell³

et al. 2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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Ancillaries have become a major source of revenue and profitability in the travel industry. Yet, conventional pricing strategies are based on business rules that are poorly optimized and do not respond to changing market conditions. This paper describes the dynamic pricing model developed by Deepair solutions, an AI technology provider for travel suppliers.We present a pricing model that provides dynamic pricing recommendations specific to each customer interaction and optimizes expected revenue per customer. The unique nature of personalized pricing provides the opportunity to search over the market space to find the optimal price-point of each ancillary for each customer, without violating customer privacy.In this paper, we present and compare three approaches for dynamic pricing of ancillaries, with increasing levels of sophistication:(1) a two-stage forecasting and optimization model using a logistic mapping function; (2) a two-stage model that uses a deep neural network for forecasting, coupled with a revenue maximization technique using discrete exhaustive search; (3) a single-stage end-to-end deep neural network that recommends the optimal price. We describe the performance of these models based on both offline and online evaluations. We also measure the real-world business impact of these approaches by deploying them in an A/B test on an airline's internet booking website. We show that traditional machine learning techniques outperform human rule-based approaches in an online setting by improving conversion by 36% and revenue per offer by 10%. We also provide results for our offline experiments which show that deep learning algorithms outperform traditional machine learning techniques for this problem. Our end-to-end deep learning model is currently being deployed by the airline in their booking system.

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Section: Customized Loss Function For Dnn-clmentioning

confidence: 99%

Section: Offline Metricsmentioning

confidence: 99%

Section: Offline Experimentsmentioning

confidence: 99%

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Dynamic Pricing for Airline Ancillaries with Customer Context

Shukla

Kolbeinsson

Otwell³

et al. 2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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“…As a platform provider, Airbnb does not control how their hosts set prices for their postings, yet it gives an assortment of apparatuses to enable their hosts to put their prices all the more adequately. For instance, they permit hosts to set altered day by day rates, end of the week costs, and limits for long haul stays, so the base price determination becomes an essential process [2].…”

Section: Introductionmentioning

confidence: 99%

“…To be able to determine the property price, some researchers use various methods with three components to determine the price: i) a binary classification model predicts the booking probability of each listing night, ii) a regression model predicts the ideal cost for each listing night, iii) personalization reasoning on top of the yield from the resulting model to deliver the last cost suggestions [2].…”

Section: Introductionmentioning

confidence: 99%

An Extreme Learning Machine Model Approach on Airbnb Base Price Prediction

Priambodo¹,

Sihabuddin²

2020

IJACSA

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The base price of Airbnb properties prediction is still a new area of prediction research, especially with the Extreme Learning Machine (ELM). The previous studies had several suggestions for the advantages of ELM, such as good generalization performance, fast learning speed, and high prediction accuracy. This paper proposes how the ELM approach is used as a prediction model for Air BnB base price. Generally, the steps are setting hidden neuron numbers, randomly assigning input weight and hidden layer biases, calculating the output layer; and the entire learning measure finished through one numerical change without iteration. The performance of the model is estimated utilizing mean squared error, mean absolute percentage error, and root mean squared error. Experiment with Airbnb dataset in London with twentyone features as input generates a faster learning speed and better accuracy than the existing model.

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The Voice of Major E-Tourism Players: An Expedia Group Perspective

Krasnodebski¹

2021

Handbook of E-Tourism

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Customized Regression Model for Airbnb Dynamic Pricing

Cited by 63 publications

References 15 publications

Dynamic Pricing for Airline Ancillaries with Customer Context

Dynamic Pricing for Airline Ancillaries with Customer Context

An Extreme Learning Machine Model Approach on Airbnb Base Price Prediction

The Voice of Major E-Tourism Players: An Expedia Group Perspective

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