A Hybrid Recommender System for Improving Rating Prediction of Movie Recommendation
Nikorn Kannikaklang, Sartra Wongthanavasu, Wachirawut Thamviset
Abstract
Because of COVID-19 pandemic, online movies are now extremely popular. While the movie theaters have not serviced and people are staying quarantine, movies are the best choice for relaxing and treating stress. In present, recommender systems are widely integrated into many platforms of movie applications. A hybrid recommender system is one promising technique to improve the system performance, especially for cold-start, data sparsity, and scalability. This paper proposed a hybrid of matrix factorization, biased matrix factorization, and factor wise matrix factorization to solve all mentioned drawback problems. Simulation shows that the proposed hybrid algorithm can decrease approximately 11.91% and 10.70% for RMSE and MAE, respectively, when compared with the traditional methods. In addition, the proposed algorithm is capable of scalability. While the number of datasets is tremendously increased by 10 times, it is still effectively executed.