A novel efficient personalized movie recommendation system with user-selectable algorithms and dynamic hybrid optimization

Most contemporary young people enjoy watching movies, which can help people relax. However, there are many different types of movies nowadays, and hundreds of new films are released domestically and internationally every year. As a result, it takes a lot of time and energy to find a favorite movie among these dazzling options. The current recommendation systems on the market cannot adjust their algorithms based on user preferences. Therefore, we propose a movie recommendation system that allows users to choose according to their needs. This system utilizes user data and preferences and employs recommendation algorithm technology combined with the characteristics of the movies. The system is primarily based on collaborative filtering (CF) and content-based filtering (CBF), utilizing the MovieLens 1M dataset. By calculating user similarity and movie similarity, the system generates recommendations for users. This helps suggest movies they are likely to be interested in, thereby improving efficiency and reducing the time spent searching for movies.