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

- This study identified language discrepancies between travelers and marketers.
- Four key facets of travel experiences were investigated across 11 destinations.
- A set of text data mining methods was applied to identify the language differences.

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.