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ABSTRACT
The increasing popularity of streaming services, which has led to a vast amount of available content. This abundance of choices can be overwhelming for users, and a good recommendation system can help them discover new movies and TV shows that they may enjoy. This paper presents a novel method for improving the accuracy of movie recommendation systems. The proposed method utilizes both collaborative filtering and content-based techniques to provide more personalized recommendations to users.
We evaluated the effectiveness of the proposed method using a dataset of movie ratings and features, and compared the results to those obtained using traditional collaborative filtering and content-based recommendation methods. Our results showed that the proposed method outperformed both baseline approaches, achieving significantly lower root mean squared error and mean squared error values
