Movie Recommender System using Improvised Cuckoo Search
Puspanjali Mohapatra1, Ritesh Kumar Mohapatra2, Bibhuranjan Sandhibigraha3
1Prof. Puspanjali Mohapatra, Department of Computer Science and Engineering, International Institute of Information Technology, Bhubaneswar (Odisha), India.
2Ritesh Kumar Mohapatra, Department of Computer Science and Engineering, International Institute of Information Technology, Bhubaneswar (Odisha), India.
3Bibhuranjan Sandhibigraha, Department of Computer Science and Engineering, International Institute of Information Technology, Bhubaneswar (Odisha), India.
Manuscript received on 01 May 2019 | Revised Manuscript received on 15 May 2019 | Manuscript published on 30 May 2019 | PP: 2869-2873 | Volume-8 Issue-7, May 2019 | Retrieval Number: G6072058719/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Recommender system is a tool for information filtering that predicts the rating for users and items, on the basis of their likings. Movie recommendation systems provides a mechanism to classify users with similar interests. This makes it an integral part of websites and e-commerce applications. In this research article, a new recommender system has been proposed which makes use of k-means clustering by adopting cuckoo search optimization algorithm applied on the Movielens dataset. Our approach has been explained systematically, and the subsequent results have been discussed. It is also compared with the existing approaches, and the results have been analysed and interpreted.
Keyword: Recommender System, Movie, Cuckoo Search, K-mean Clustering.
Scope of the Article: Program Understanding and System Maintenance