Authors: V. Subramaniyaswamy; R. Logesh; M. Chandrashekhar; Anirudh Challa; V. Vijayakumar
Addresses: School of Computing, SASTRA University, Thanjavur, India ' School of Computing, SASTRA University, Thanjavur, India ' School of Computing, SASTRA University, Thanjavur, India ' School of Computing, SASTRA University, Thanjavur, India ' School of Computing Science and Engineering, VIT University, Chennai, India
Abstract: Over the last decade, there has been a burgeoning of data due to social media, e-commerce and overall digitisation of enterprises. The data is exploited to make informed choices, predict marketplace trends and patterns in consumer preferences. Recommendation systems have become ubiquitous after the penetration of internet services among the masses. The idea is to make use of filtering and clustering techniques to suggest items of interest to users. For a media commodity like movies, suggestions are made to users by finding user profiles of individuals with similar tastes. Initially, user preference is obtained by letting them rate movies of their choice. Upon usage, the recommender system will be able to understand the user better and suggest movies that are more likely to be rated higher. The experiment results on the MovieLens dataset provides a reliable model which is precise and generates more personalised movie recommendations compared to other models.
Keywords: data mining; collaborative filtering; movie recommendations; film recommendations; data acquisition; personalisation; personalised recommendations; recommendation systems; recommender systems; films; movies; user preference.
International Journal of High Performance Computing and Networking, 2017 Vol.10 No.1/2, pp.54 - 63
Available online: 13 Mar 2017 *Full-text access for editors Access for subscribers Purchase this article Comment on this article