Forecasting hourly attraction tourist volume with search engine and social media data for decision support-Reference-Cited by-同舟云学术

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Forecasting hourly attraction tourist volume with search engine and social media data for decision support

Published:2023-07 Issue:4 Volume:60 Page:103399
ISSN:0306-4573
Container-title:Information Processing & Management
language:en
Short-container-title:Information Processing & Management

Author:

Xue Gang,Liu Shifeng,Ren Long,Gong Daqing

Funder

Beijing Logistics Informatics Research Base

Ministry of Education Key Projects of Philosophy and Social Sciences Research

Beijing Municipal Social Science Foundation

National Natural Science Foundation of China

Ministry of Education of the People's Republic of China

Natural Science Foundation of Beijing Municipality

Beijing Social Science Fund

Publisher

Elsevier BV

Subject

Library and Information Sciences,Management Science and Operations Research,Computer Science Applications,Media Technology,Information Systems

Reference64 articles.

1. A semantic enhanced hybrid recommendation approach: A case study of e-Government tourism service recommendation system;Al-Hassan;Decision Support Systems,2015

2. Tourism demand forecasting with time series imaging: A deep learning model;Bi;Annals of Tourism Research,2021

3. Daily tourism volume forecasting for tourist attractions;Bi;Annals of Tourism Research,2020

4. Spurious patterns in Google Trends data - An analysis of the effects on tourism demand forecasting in Germany;Bokelmann;Tourism Management,2019

5. Forecasting seasonal tourism demand using a multiseries structural time series method;Chen;Journal of Travel Research,2017

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