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Title: Popularity prediction on vacation rental websites
Authors: Li, Yang
Wang, Suhang
Ma, Yukun
Pan, Quan
Cambria, Erik
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Li, Y., Wang, S., Ma, Y., Pan, Q. & Cambria, E. (2020). Popularity prediction on vacation rental websites. Neurocomputing, 412, 372-380.
Project: A18A2b0046
Journal: Neurocomputing
Abstract: In the personal house renting scenario, customers usually make quick assessments based on previous customers' reviews, which makes such reviews essential for the business. If the house is assessed as popular, a Matthew effect will be observed as more people will be willing to book it. Due to the lack of definition and quantity assessment measures, however, it is difficult to make a popularity evaluation and prediction. To solve this problem, the concept of house popularity is well defined in this paper. Specifically, the house popularity is decided by inter-event timeand rating score at the same time. To make a more effective prediction over these two correlated variables, a dual-gated recurrent unit (DGRU) is employed. Furthermore, an encoder-decoder framework with DGRU is proposed to perform popularity prediction. Empirical results show the effectiveness of the proposed DGRU and the encoder-decoder framework in two-correlated sequences prediction and popularity prediction, respectively.
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2020.05.092
Schools: School of Computer Science and Engineering 
Rights: © 2020 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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