Recommender systems are valuable tools for the tourism development of emerging destinations, as they provide useful suggestions and facilitate travel planning. However, the lack of adequate data limits their creation, so it is necessary to develop clear methodologies that allow these destinations to plan and create their own recommendation systems, optimizing data management to improve the tourism offer and provide safe tourism for tourists. This paper presents a novel methodology for the construction of Recommender systems using Multicriteria Decision Aiding (MCDA) methods, in particular the Logic Scoring of Preference (LSP) method, designed for growing destinations. The proposed methodology facilitates the development of recommendation systems using different data sources and defining relevant criteria and attributes that fit into a hierarchical framework. The presented model allows the development of an adaptable, user-friendly and intuitive web system that provides personalized recommendations based on each user’s preferences. The relevance of the recommendations was positively evaluated by 86.42% of the users and the usability of the system was praised by 70.37%. The results of the application demonstrate the relevance of the proposed methodology to improve the user experience and provide relevant and individualized recommendations in emerging tourism destinations.