DeepPredict: A Zone Preference Prediction System for Online Lodging Platforms

Journal of Social Computing

Hui Pan¹  Ma Yihan²  Sun Hua²  Zhang Jiayun²  Chen Yang²  Xu Yang²  Wang Xin² 

[1] Department of Computer Science, University of Helsinki, Helsinki 00014, Finland;School of Computer Science, Fudan University, Shanghai 200433, China;

关键词: online lodging platform;    zone preference;    prediction;    deep learning;   

DOI  :  10.23919/JSC.2021.0004

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown