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Journal of Social Computing 2021, 2(1): 52-70 https://doi.org/10.23919/JSC.2021.0004
Open Access | Issue | Published: 16 February 2021

DeepPredict: A Zone Preference Prediction System for Online Lodging Platforms

Show Author's Information Yihan Ma,  Hua Sun,  Yang Chen( ),  Jiayun Zhang,  Yang Xu,  Xin Wang,  Pan Hui
School of Computer Science, Fudan University, Shanghai 200433, China
Department of Computer Science, University of Helsinki, Helsinki 00014, Finland
Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China
Keywords:
deep learning, prediction, online lodging platform, zone preference
Cite this article:
Ma Y, Sun H, Chen Y, et al. DeepPredict: A Zone Preference Prediction System for Online Lodging Platforms. Journal of Social Computing, 2021, 2(1): 52-70. https://doi.org/10.23919/JSC.2021.0004
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Abstract Full text About this article

Online lodging platforms have become more and more popular around the world. To make a booking in these platforms, a user usually needs to select a city first, then browses among all the prospective options. To improve the user experience, understanding the zone preferences of a user’s booking behavior will be helpful. In this work, we aim to predict the zone preferences of users when booking accommodations for the next travel. We have two main challenges: (1) The previous works about next information of Points Of Interest (POIs) recommendation are mainly focused on users’ historical records in the same city, while in practice, the historical records of a user in the same city would be very sparse. (2) Since each city has its own specific geographical entities, it is hard to extract the structured geographical features of accommodation in different cities. Towards the difficulties, we propose DeepPredict, a zone preference prediction system. To tackle the first challenge, DeepPredict involves users’ historical records in all the cities and uses a deep learning based method to process them. For the second challenge, DeepPredict uses HERE places API to get the information of POIs nearby, and processes the information with a unified way to get it. Also, the description of each accommodation might include some useful information, thus we use Sent2Vec, a sentence embedding algorithm, to get the embedding of accommodation description. Using a real-world dataset collected from Airbnb, DeepPredict can predict the zone preferences of users’ next bookings with a remarkable performance. DeepPredict outperforms the state-of-the-art algorithms by 60% in macro F1-score.


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DeepPredict: A Zone Preference Prediction System for Online Lodging Platforms

Show Author's information Yihan Ma, Hua Sun, Yang Chen( ), Jiayun Zhang, Yang Xu, Xin Wang, Pan Hui
School of Computer Science, Fudan University, Shanghai 200433, China
Department of Computer Science, University of Helsinki, Helsinki 00014, Finland
Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China

Abstract

Online lodging platforms have become more and more popular around the world. To make a booking in these platforms, a user usually needs to select a city first, then browses among all the prospective options. To improve the user experience, understanding the zone preferences of a user’s booking behavior will be helpful. In this work, we aim to predict the zone preferences of users when booking accommodations for the next travel. We have two main challenges: (1) The previous works about next information of Points Of Interest (POIs) recommendation are mainly focused on users’ historical records in the same city, while in practice, the historical records of a user in the same city would be very sparse. (2) Since each city has its own specific geographical entities, it is hard to extract the structured geographical features of accommodation in different cities. Towards the difficulties, we propose DeepPredict, a zone preference prediction system. To tackle the first challenge, DeepPredict involves users’ historical records in all the cities and uses a deep learning based method to process them. For the second challenge, DeepPredict uses HERE places API to get the information of POIs nearby, and processes the information with a unified way to get it. Also, the description of each accommodation might include some useful information, thus we use Sent2Vec, a sentence embedding algorithm, to get the embedding of accommodation description. Using a real-world dataset collected from Airbnb, DeepPredict can predict the zone preferences of users’ next bookings with a remarkable performance. DeepPredict outperforms the state-of-the-art algorithms by 60% in macro F1-score.

Keywords: deep learning, prediction, online lodging platform, zone preference

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Publication history

Received: 20 December 2020
Accepted: 22 January 2021
Published: 16 February 2021
Issue date: March 2021

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© The author(s) 2021

Acknowledgements

This work was sponsored by the National Natural Science Foundation of China (Nos. 71731004, 62072115, 61602122, and 61971145), Shanghai Pujiang Program (No. 2020PJD005), the Research Grants Council of Hong Kong (No. 16214817), and the 5GEAR Project and FIT Project from the Academy of Finland.

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