Treffer: Machine Learning-Based Dynamic Context Real-Time Movie Recommendation System

This chapter is an advanced film recommendation model that transforms the way users discover and interact with movies. Using avant-garde algorithms, it dynamically analyses user preferences and the visualization of stories and grades to offer highly personalized film suggestions. By participating in natural and interactive dialogues, the recommendations of the model's tailors are based on various criteria, including genres, actors, directors, and thematic elements. The objective is to simplify selecting films, improving user satisfaction by providing cured suggestions that align with individual tastes. When examining the key attributes obtained through the interactions and user feedback, this study evaluates the effectiveness of the different automatic learning models and natural language processing techniques in delivering precise recommendations. A comparative analysis of several algorithms is performed, including collaborative filtering, content based on content and approaches based on deep learning, to determine the optimal balance between precision, computational efficiency, and interpretability. The model of the model to adapt and learn from the user's behaviour guarantees continuous improvement in the quality of the recommendation, so it is a powerful tool for film enthusiasts. The results demonstrate that the AI model, after rigorous evidence against multiple models such as random forests, neuronal networks, and transformers-based architectures, achieves a 94%accuracy, establishing its effectiveness in delivering recommendations of high-quality user-centred movies.