Smart Recommendation System for Tourists

Abstract

This study aims to develop a smart recommendation system that assists tourists in selecting their travel destinations based on personal interests, by integrating artificial intelligence techniques in analyzing preferences and reviews. A comprehensive scientific methodology was adopted, beginning with the identification of user needs, followed by data collection on tourist attractions and user evaluations, and culminating in the design of a smart recommendation model based on the Matrix Factorization algorithm within the ML.NET framework.
Context awareness was incorporated to enhance the precision of suggestions. The system was developed using ASP.NET Core MVC and SQL Server, which contributed to the efficiency of the recommendations and improved user interaction. The proposed model demonstrated significant superiority in recommendation accuracy compared to traditional systems, helped reduce the confusion caused by an overload of options, and increased user satisfaction—affirming its effectiveness in supporting informed tourist decision-making and guiding travelers toward more personalized and satisfying experiences.