The final thesis presents a comprehensive exploration of two recommender system models: a content-based model and a collaborative filtering model. The content-based model encompasses the definition of product and user profiles, as well as the establishment of a similarity function. The collaborative filtering model introduces the concept of neighborhood, similarity function, and prediction calculation. To enhance the recommendation process for movies, a hybrid model is introduced, building upon the foundation of the content-based model and incorporating the strengths of collaborative filtering. This hybrid approach effectively addresses challenges such as the cold start problem and over-specialization, resulting in improved movie recommendations. The MovieLens dataset is carefully refined and adapted to ensure its suitability for the study. This dataset encompasses a wealth of information, including 1,000,209 user ratings for movies and data pertaining to 3,900 films. Through the implementation of a programmatic movie recommendation model based on content features, the accuracy of the model is evaluated. Additionally, a hybrid model for movie recommendation is implemented, and its accuracy is compared to that of the content-based model. Finally, the results are thoroughly examined and discussed to provide valuable insights and conclusions.