Efficient Hybrid Movie Recommendation System Framework Based on A Sequential Model

A recommendation system is a system that offers suggestions to users, leveraging specific data such as books, movies, songs, and other relevant information. Movie recommendation algorithms utilize the attributes of previously enjoyed films to predict the preferences of users and suggest similar movies they might enjoy. Businesses that gather ample customer data and strive to deliver top-notch recommendations can greatly benefit from these recommendation systems. When creating a movie recommendation system, multiple elements such as genre, cast, and even the director of the movie are taken into account. This paper introduces a hybrid movie recommendation system that utilizes a combination of weighted average and min-max scaler to assess movie ratings and popularity. Moreover, TF-IDF is utilized for transforming the data into vectors, while cosine similarity is employed to gauge the resemblance among these vectors. The recommender system is built using the Movies dataset. The results show the top-K recommendation for users as well as the proposed system can provide a prediction of rating for a particular movie.