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Points of Interest Recommendations Based on Check-In Motivations

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During a trip, tourists are mostly dependent on their mobile phone to select their next points of interest (POI). A mobile application that recommends POIs such as tourist attractions or restaurants is based on the user's location data such as check-in history. This article recommends a novel approach to leverage the check-in data captured by location-based social networks (LBSNs) with an aim to improve POI recommendations through personalized explanations. The proposed algorithm generates a user's motivation profile, and its applicability is presented by analyzing a dataset extracted from a popular LBSN. A between-subject experiment (N = 182) is conducted that shows explanations generated using a user's motivation profile increase transparency, which leads to intent to use the LBSN. Perceived usefulness of the LBSN also increases intent to use. The study indicates that when suggesting a POI, recommender system developers include explanations based on user's motivation behavior profile.
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Document Type: Research Article

Publication date: May 15, 2019

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  • The aim of Tourism Analysis is to promote a forum for practitioners and academicians in the fields of Leisure, Recreation, Tourism, and Hospitality (LRTH). As a interdisciplinary journal, it is an appropriate outlet for articles, research notes, and computer software packages designed to be of interest, concern, and of applied value to its audience of professionals, scholars, and students of LRTH programs the world over.
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