Title: A hybrid model collaborative movie recommendation system using K-means clustering with ant colony optimisation
Authors: M. Sandeep Kumar; J. Prabhu
Addresses: Department of Department of Software System and Engineering, School of Information Technology and Engineering, Vellore Institute of Technology, Vellore-632014, Tamil Nadu, India ' Department of Department of Software System and Engineering, School of Information Technology and Engineering, Vellore Institute of Technology, Vellore-632014, Tamil Nadu, India
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.
Keywords: recommender system; nearest neighbour; ant colony optimisation; ACO; cluster; K-means; collaborative filtering.
International Journal of Internet Technology and Secured Transactions, 2020 Vol.10 No.3, pp.337 - 354
Received: 04 Apr 2018
Accepted: 31 May 2018
Published online: 04 May 2020 *