 Assessing language discrepancies between travelers and online travel recommendation systems: Application of the Jaccard distance score to web data miningSangwon Park and Dae-Young Kim Technological Forecasting and Social Change, 2017, vol. 123, issue C, 381-388 Abstract: By using a human-centric approach to online recommender systems, this research aims to estimate the language discrepancies of which travelers and destination marketers describe the travel experiences across 11 tourism destinations in USA. In order to address the research purpose, data has been collected from two different sources that reflect the views of travelers and service providers. Then, a set of text data mining methods (i.e., clustering analysis and Jaccard distance score) was applied to identify the language differences between travelers and CVB websites, according to the following categories: shopping, dining, nightlife/activities, and attractions. Some possible methodological extensions that can improve recommendation capabilities, and managerial implications of these findings are provided. Keywords: Smart tourism; Online recommender system; Web data mining; Jaccard distance score (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:123:y:2017:i:c:p:381-388 DOI: 10.1016/j.techfore.2017.03.031 Access Statistics for this article Technological Forecasting and Social Change is currently edited by Fred Phillips More articles in Technological Forecasting and Social Change from Elsevier |
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