Assessing language discrepancies between travelers and online travel recommendation systems: Application of the Jaccard distance score to web data mining

Sangwon Park; Dae Young Kim

doi:10.1016/j.techfore.2017.03.031

Assessing language discrepancies between travelers and online travel recommendation systems: Application of the Jaccard distance score to web data mining

Sangwon Park, Dae Young Kim

Research output: Journal article publication › Journal article › Academic research › peer-review

18 Citations (Scopus)

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.

Original language	English
Pages (from-to)	381-388
Number of pages	8
Journal	Technological Forecasting and Social Change
Volume	123
DOIs	https://doi.org/10.1016/j.techfore.2017.03.031
Publication status	Published - 1 Oct 2017
Externally published	Yes

Keywords

Jaccard distance score
Online recommender system
Smart tourism
Web data mining

ASJC Scopus subject areas

Business and International Management
Applied Psychology
Management of Technology and Innovation

More information

10.1016/j.techfore.2017.03.031

Cite this

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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.",

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Assessing language discrepancies between travelers and online travel recommendation systems: Application of the Jaccard distance score to web data mining. / Park, Sangwon; Kim, Dae Young.
In: Technological Forecasting and Social Change, Vol. 123, 01.10.2017, p. 381-388.

Research output: Journal article publication › Journal article › Academic research › peer-review

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T1 - Assessing language discrepancies between travelers and online travel recommendation systems: Application of the Jaccard distance score to web data mining

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AU - Kim, Dae Young

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AB - 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.

KW - Jaccard distance score

KW - Online recommender system

KW - Smart tourism

KW - Web data mining

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DO - 10.1016/j.techfore.2017.03.031

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JO - Technological Forecasting and Social Change

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Assessing language discrepancies between travelers and online travel recommendation systems: Application of the Jaccard distance score to web data mining

Abstract

Keywords

ASJC Scopus subject areas

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