Mostovyi, Oleksandr2024-02-282024-02-282024https://er.auk.edu.ua/handle/234907866/46The main functionality for all dating projects is to elevate user experience through the provision of potential matches based on a variety of factors. This project is dedicated to evaluating and comparing the performance of diverse recommendation algorithms within the unique context of dating platforms. As a result an environment consisting of recommendation system and integration into search system of dating application is provided. The evaluation metrics include but are not limited to accuracy, precision, recall, and user satisfaction. By systematically testing these algorithms in controlled scenarios, the research seeks to identify specific performance strengths and weaknesses of each algorithm. Recommendation system is implemented using Python programming language and Flask framework and it uses dataset of profile from dating project. The research also explores the impact of diverse user behaviors and preferences on the recommendation algorithms, providing insights into the adaptability and robustness of each approach. Through a comprehensive analysis, this research aims to contribute valuable insights for dating projects seeking to enhance their recommendation systems. The findings will aid in the informed selection of recommendation algorithms tailored to the specific requirements and dynamics of dating platforms, ultimately improving user experience and satisfaction.en-USThesis