Difference between revisions of "Wikipedia in the Tourism Industry: Forecasting Demand and Modeling Usage Behavior"
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| − | '''Wikipedia in the Tourism Industry: Forecasting Demand and Modeling Usage Behavior''' - scientific work related to Wikipedia quality published in 2016, written by Pejman Khadivi and Naren Ramakrishnan. | + | '''Wikipedia in the Tourism Industry: Forecasting Demand and Modeling Usage Behavior''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Pejman Khadivi]] and [[Naren Ramakrishnan]]. |
== Overview == | == Overview == | ||
| − | Due to the economic and social impacts of tourism, both private and public sectors are interested in precisely forecasting the tourism demand volume in a timely manner. With recent advances in social | + | Due to the economic and social impacts of tourism, both private and public sectors are interested in precisely forecasting the tourism demand volume in a timely manner. With recent advances in [[social network]]s, more people use online resources to plan their future trips. In this paper authors explore the application of [[Wikipedia]] usage trends (WUTs) in tourism analysis. Authors propose a framework that deploys WUTs for forecasting the tourism demand of Hawaii. Authors also propose a data-driven approach, using WUTs, to estimate the behavior of tourists when they plan their trips. |
Revision as of 19:26, 7 February 2021
Wikipedia in the Tourism Industry: Forecasting Demand and Modeling Usage Behavior - scientific work related to Wikipedia quality published in 2016, written by Pejman Khadivi and Naren Ramakrishnan.
Overview
Due to the economic and social impacts of tourism, both private and public sectors are interested in precisely forecasting the tourism demand volume in a timely manner. With recent advances in social networks, more people use online resources to plan their future trips. In this paper authors explore the application of Wikipedia usage trends (WUTs) in tourism analysis. Authors propose a framework that deploys WUTs for forecasting the tourism demand of Hawaii. Authors also propose a data-driven approach, using WUTs, to estimate the behavior of tourists when they plan their trips.
