Movie Recommender Model Using Machine Learning Approaches

Raja Marappan; S. Bhaskaran

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

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Article Open Access http://dx.doi.org/10.26855/er.2022.07.006

Movie Recommender Model Using Machine Learning Approaches

Raja Marappan^*, S. Bhaskaran

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

*Corresponding author: Raja Marappan

Published: August 2,2022

Abstract

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.

References

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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.

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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.

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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, USA, 6(7), 317-319.

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

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Movie Recommender Model Using Machine Learning Approaches

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