Abstract
Online lodging platforms have become very popular around the world. To make a booking, a user normally needs to select a city first, then browses among prospective options. To improve the user experience, understanding the location preference of a user’s booking behavior will be useful. In this paper, we propose DeepLoc, a location preference prediction system, adopting deep learning technologies to predict the location preference of a user’s next booking, based on both the descriptive features and the user’s historical booking records. Using the real data collected from Airbnb, we can see that DeepLoc can achieve an F1-score of 0.885 for booking apartments in the city of London.
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Notes
- 1.
https://www.homestay.com/, accessed on May 1, 2019.
- 2.
https://press.airbnb.com/about-us/, accessed on May 1, 2019.
- 3.
InsideAirbnb (http://insideairbnb.com/, accessed on May 1, 2019) is a website which offers open sourced dataset contains the detailed information of the accommodations and reviews in 84 cities in Airbnb.
- 4.
If an account is deleted by the corresponding user or by the Airbnb platform, the profile page of this user will be unavailable.
- 5.
https://www.london.gov.uk/what-we-do/planning/london-plan, accessed on May 1, 2019.
- 6.
The types of Airbnb accommodations, including apartments, villas, tree houses.
- 7.
There are three types of rooms in Airbnb: Private room, Shared room and Entire home.
- 8.
It refers to the number of people that this accommodations can host at one time.
- 9.
https://developers.google.com/places/web-service/intro, accessed on May 1, 2019.
- 10.
The reviews user received from her guests.
- 11.
https://keras.io/, accessed on May 1, 2019.
- 12.
https://www.tensorflow.org/, accessed on May 1, 2019.
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Acknowledgment
This work is sponsored by National Natural Science Foundation of China (No. 61602122, No. 71731004), the Research Grants Council of Hong Kong (No. 16214817) and the 5GEAR project from the Academy of Finland.
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Ma, Y. et al. (2019). DeepLoc: A Location Preference Prediction System for Online Lodging Platforms. In: Sun, Y., Lu, T., Yu, Z., Fan, H., Gao, L. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2019. Communications in Computer and Information Science, vol 1042. Springer, Singapore. https://doi.org/10.1007/978-981-15-1377-0_48
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DOI: https://doi.org/10.1007/978-981-15-1377-0_48
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