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

Movie Recommender Model Using Machine Learning Approaches

Date: August 2,2022 |Hits: 265 Download PDF How to cite this paper

Raja Marappan*, S. Bhaskaran

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

*Corresponding author: Raja Marappan

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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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

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