Adaptive Personalized Tourist Recommendation Application Platform and Community-Based Tourism Management of Takhian Tia Community Banglamung Chonburi
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Abstract
Community-based tourism focuses on enhancing the value of cultural and natural resources of the community. Tourists get an authentic experience from the local lifestyle and wisdom within the community. Therefore, having an application that recommends tourist spots personalized to individual tourists' needs, and a community tourism management system is essential. This research presents the development of an application platform for an adaptive personalized tourist attraction recommendation system (APTARS). The objective is to create an adaptive personalized tourist attraction recommendation model using a collaborative filtering method and developing cross-platform applications for tourists and entrepreneurs in community-based tourism management on Android and iOS operating systems, developed with React, Node.js, and Flutter, managing databases with MongoDB. The researcher evaluated the performance of the adaptive personalized tourist attraction recommendation model using the Mean Absolute Error value (MAE) and Root Mean Square Error (RMSE). The research findings indicate that the Euclidean distance is the most effective algorithm to measure user similarity for optimal recommendation performance. The appropriate number of neighboring nodes for travel destination recommendation is 25 nodes. The performance evaluation of the application platform by experts is rated as good. User satisfaction assessment of the application platform from a sample group of 40 people, comprising 10 community entrepreneurs and 30 tourists, found that the overall user satisfaction is rated very good (=4.52, S.D.=0.51). The accuracy and functional performance satisfaction had the highest mean (
=4.56, S.D.=0.52), followed by satisfaction with the ability to work according to user needs (
=4.53, S.D.=0.49), satisfaction with system usage (
=4.50, S.D.=0.52), and satisfaction with system and information security (
=4.48, S.D.=0.51) respectively.
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References
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