Improving User Experience in Movie Recommendations Using Machine Learning

For movie recommendation systems, conventional methodologies, such as content-based approaches and collaborative filtering, have cold-start problems and too specific recommendations. These limitations make recommendations less personalized and negatively affect user experience. The proposed approach overcomes these drawbacks by combining the traditional collaborative filtering with deep learning approaches, such as recurrent neural networks (RNN) and neural collaborative filtering (NCF). It captures complicated user preferences and adapts to dynamic user behaviors, thus increasing the accuracy of the recommendations. With the higher precision, 80.1%, and recall, 76.6%, compared to the traditional approaches, the proposed system outperforms the target methods, and evaluation results demonstrate significant improvements in their outcomes. In particular, the proposed system has an average score of 4.5 and longer duration of the watch time. The mean squared error is reduced to 0.35, and the satisfaction levels are higher. In other words, as the recommendation system suggests more engaging and relevant movies, it substantially increases the overall user experience.