BUILDING A MOVIE RECOMMENDATION SYSTEM USING COLLABORATIVE FILTERING METHOD.

Abstract

These days, consumers frequently employ a variety of movie-watching platforms, such as smart TVs and internet apps, with ever-increasing content libraries. Search time will be reduced and user experience will be enhanced by offering recommendations that are consistent and useful across all of these platforms. Data mining approaches from various sources, data analysis, and machine learning model building based on collaborative filtering, content filtering, and ensemble methods are all part of the research methodology. Additionally, deep neural networks and reinforcement learning algorithms are used to develop a highly personalized and adaptive recommendation model that operates on several platforms. Results demonstrate that the recommender system can make more accurate movie recommendations, increasing user engagement and happiness. The study's conclusion confirms that recommender systems represent a significant advancement in the entertainment sector, improving user experience while creating the possibility of creating intelligent, scalable, and adaptable recommender systems for a wide range of future applications.