Hill Publishing Group | contact@hillpublisher.com

Hill Publishing Group

Location:Home / Journals / The Educational Review, USA /


Movie Recommender Model Using Machine Learning Approaches

Date: August 2,2022 |Hits: 3659 Download PDF How to cite this paper

Raja Marappan*, S. Bhaskaran

School of Computing, SASTRA Deemed University, Thanjavur, India.

*Corresponding author: Raja Marappan


There are different suggestions or information filtering systems developed to solve real-world problems. The recommendation systems are performing the role of information filtering in different scenarios. To provide a better recommendation, different soft computing strategies such as machine learning and evolutionary computing are applied. The recommendation systems fulfill the requirements of the users on time. Concerning organizations, the company likes to keep their users long on the platforms to maximize the profit. Better recommendations are expected to generate positive feedback for both users and organizations. One of the most widely used real-world applications is the movies in which the users are expecting better information filtering. The movie recommender system is expected to predict the preferred items of the user based on the similarity ratings of other people. This article focuses on developing the movie recommendation model using machine learning approaches—the count vectorizer and nearest neighbors approaches.


Bhaskaran, S., Marappan, R., Santhi, B. (2020).  Design and Comparative Analysis of New Personalized Recommender Algorithms with Specific Features for Large Scale Datasets. Mathematics, 2020, 8, 1106. https://doi.org/10.3390/math8071106.

Bhaskaran, S., Marappan, R., Santhi, B. (2021). Design and Analysis of a Cluster-Based Intelligent Hybrid Recommendation System for E-Learning Applications. Mathematics, 2021, 9, 197. https://doi.org/10.3390/math9020197.

G. Sethumadhavan and R. Marappan. (2013). A genetic algorithm for graph coloring using single parent conflict gene crossover and mutation with conflict gene removal procedure. 2013 IEEE International Conference on Computational Intelligence and Computing Research, 2013, pp. 1-6. doi: 10.1109/ICCIC.2013.6724190.

Harper, F. M. and Konstan, J. A. (2015). The MovieLens datasets: History and context. ACM Trans. Interact. Intell. Syst., 2015, 5, 19:1-19:19. doi:10. 1145/2827872.

Marappan, R. and Bhaskaran, S. (2022). Movie Recommendation System Modeling Using Machine Learning. International Journal of Mathematical, Engineering, Biological and Applied Computing, 2022, 1(1), 12-16. DOI: 10.31586/ijmebac.2022.291.

Marappan, R. and Sethumadhavan, G. (2018). Solution to Graph Coloring Using Genetic and Tabu Search Procedures. Arab J SciEng., 43, 525-542 (2018). https://doi.org/10.1007/s13369-017-2686-9.

Marappan, R. and Sethumadhavan, G. (2020). Complexity Analysis and Stochastic Convergence of Some Well-known Evolutionary Operators for Solving Graph Coloring Problem. Mathematics, 2020, 8, 303. https://doi.org/10.3390/math8030303.

Marappan, R. and Sethumadhavan, G. (2021). Solving Graph Coloring Problem Using Divide and Conquer-Based Turbulent Particle Swarm Optimization. Arab J SciEng, (2021). https://doi.org/10.1007/s13369-021-06323-x.

R. Marappan and G. Sethumadhavan. (2013). A New Genetic Algorithm for Graph Coloring. 2013 Fifth International Conference on Computational Intelligence, Modelling and Simulation, 2013, pp. 49-54. doi: 10.1109/CIMSim.2013.17.

Raja Marappan and S. Bhaskaran. (2022). Datasets Finders and Best Public Datasets for Machine Learning and Data Science Applications. COJ Rob Artificial Intel., 2(1). COJRA. 000530. 2022.

How to cite this paper

Movie Recommender Model Using Machine Learning Approaches

How to cite this paper: Raja Marappan, S. Bhaskaran. (2022). Movie Recommender Model Using Machine Learning Approaches. The Educational Review, USA6(7), 317-319.

DOI: http://dx.doi.org/10.26855/er.2022.07.006

Free HPG Newsletters

Add your e-mail address to receive free newsletters from Hill Publishing Group.

Contact us

Hill Publishing Group

8825 53rd Ave

Elmhurst, NY 11373, USA

E-mail: contact@hillpublisher.com

Copyright © 2019 Hill Publishing Group Inc. All Rights Reserved.