Hybrid Collaborative Movie Recommendation System
Mohamad Riduan Mas Husin, Tajul Rosli Razak, Ariff Md Ab Malik, Sharifalillah Nordin, Shuzlina Abdul-Rahman · 2023
A recommendation system applies the data to the information discovery techniques and personalises the products for recommendations. Typically, movie recommendation systems predict what movies a user wants based on the characteristics of previously liked movies. These recommendation systems are helpful for organisations that gather data from many users and wish to offer the best possible recommendations effectively. This study developed a hybrid movie recommendation system using collaborative and content-based filtering involving Matrix Factorisation, TF-IDF. In addition, this study aims to extract the CBF features into CF to improve the recommendation system engine's performance and personalise the user's movie recommendations. The performance of the trained model was measured using RMSE, precision, recall, and F1 score. The trained model produced a low RMSE value with high precision, recall, and F1 score values. The paper's contribution is that the proposed hybrid recommendation provides a pathway for the users by helping them find movies that meet their interests.