Popularity prediction on vacation rental websites

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 time and 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.

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Work Title Popularity prediction on vacation rental websites
Access
Open Access
Creators
  1. Yang Li
  2. Suhang Wang
  3. Yukun Ma
  4. Quan Pan
  5. Erik Cambria
Keyword
  1. Vacation rental websites
  2. Popularity prediction
  3. Dual-gated recurrent unit
  4. Inter-event time and rating score
License In Copyright (Rights Reserved)
Work Type Article
Publisher
  1. Neurocomputing
Publication Date July 15, 2020
Publisher Identifier (DOI)
  1. https://doi.org/10.1016/j.neucom.2020.05.092
Deposited July 25, 2022

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Version 1
published

  • Created
  • Added 1-s2.0-S0925231220309498-main.pdf
  • Added Creator Yang Li
  • Added Creator Suhang Wang
  • Added Creator Yukun Ma
  • Added Creator Quan Pan
  • Added Creator Erik Cambria
  • Published
  • Updated Keyword, Description, Publication Date Show Changes
    Keyword
    • Vacation rental websites, Popularity prediction, Dual-gated recurrent unit, Inter-event time and rating score
    Description
    • <p>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.</p>
    • <p>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 time and 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.</p>
    Publication Date
    • 2020-10-28
    • 2020-07-15