A hybrid model collaborative movie recommendation system using K-means clustering with ant colony optimisation

Abstract

Movie recommendation system offers a mechanism to allocate the user to attain the famous film by getting an opinion from similar users or past rating by user. This produces recommender systems has a crucial part of website and e-commerce application. The primary objective of the system to prefer a recommender system by data clustering and computational intelligence. We proposed a hybrid model collaborative movie recommendation system that performs with a combination of K-means clustering with ant colony optimisation technique (ACO-KM) that has employed in movie dataset. The proposed system compared with existing works, and its efforts have been analysed. The evaluation process of movie recommendation system that offers improved result from ACO-KM collaborative movie recommender system based on precision, recall, mean square error (MSE), and accuracy. By comparison of speed (in seconds) of various approaches in Movielens dataset, our approach gives best result 42.24 compared with existing one 53.22. The outcome of this experiment from Movielens dataset that offers scalability and efficiency in a recommendation by decreasing cold start issues.