A recommender system based on personal constraints for smart tourism city

In a smart tourism ecosystem, travel communication websites play a critical role in choosing destinations and hotels. This research suggests a travel recommender system for automating word-of-mouth (WOM) effects and providing personalized travel-planning services to tourists. Collaborative filtering (CF)-based recommender systems have been extensively employed for personalization services in diverse areas; the basic principle of CF is WOM communication. This research proposes a travel recommender system that helps a tourist build his/her personalized travel plan based on CF and constraint satisfaction filtering. Constraint satisfaction filtering is adopted to profile a tourist’s needs and circumstances. For this purpose, this research modifies the existing constraint satisfaction method to an approximate constraint satisfaction filtering method that incorporates indifference intervals into constraints. We build a prototype system and a benchmark system to evaluate the effectiveness, usability, and novelty of the proposed travel recommender system. The experimental results demonstrate a methodology for performing personalized tourist’s travel planning and automating WOM communication outperforms the benchmark system.