From Reviews to Recommendations: Discovering Latent Visitor Preferences for Sustainable Wellness Templestay Management

The sustainability of experience-intensive wellness tourism services increasingly depends on managers’ ability to understand heterogeneous and implicit tourist preferences that are rarely captured through traditional survey-based approaches. In the context of Korean Templestay tourism, this study develops a data-driven decision-support framework that leverages large-scale unstructured review data to address managerial challenges such as choice overload, inefficient resource allocation, and cold-start conditions. Using 74,015 user-generated reviews collected between 2020 and 2024, the framework integrates Optical Character Recognition (OCR) to extract image-embedded text, achieving a validated character-level accuracy of 96.8%. In addition, a weak supervision strategy is applied to identify latent tourist preferences in a cost-efficient and scalable manner. Preference classification is conducted using Random Forest models combined with SMOTE, followed by clustering and user-based collaborative filtering to support personalized recommendations. The findings indicate that the Templestay market is better understood as an interconnected preference network rather than a set of mutually exclusive segments. Across user groups, “rest” emerges as a shared foundational value, while differentiated sub-preferences coexist within the network. The proposed framework successfully generates recommendations for all users in the dataset, demonstrating strong applicability for mitigating cold-start risks and supporting adaptive and sustainable program design.