Points of Interest Recommendations Based on Check-In Motivations
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.
Keywords: EXPLANATIONS; LOCATION-BASED SOCIAL NETWORKS (LBSNS); MOTIVATION AWARE; POINTS OF INTEREST RECOMMENDATIONS
Document Type: Research Article
Publication date: 15 May 2019
- Established in 1996, Tourism Analysis is an interdisciplinary journal that provides a platform for exchanging ideas and research in tourism and related fields. The journal aims to publish articles that explore a broad range of research subjects, including, but not limited to, the social, economic, cultural, environmental, and psychological aspects of tourism, consumer behavior in tourism, sustainable and responsible tourism, and effective operations, marketing, and management.
Tourism Analysis focuses on both theoretical and applied research and strives to promote innovative approaches to understanding the complex and dynamic nature of tourism, its stakeholders, businesses, and its effects on society. The journal welcomes articles on innovative research topics and methodologies beyond the traditional theory-testing sciences, such as robotics, computational sciences, and data analytics.
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