Home > Archive > 2016 > Volume 6 Number 2 (Apr. 2016) >
IJMLC 2016 Vol.6(2): 97-100 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2016.6.2.580

A Multinomial Probabilistic Model for Movie Genre Predictions

Eric Arnaud Makita Makita and Artem Lenskiy

Abstract—This paper proposes a movie genre-prediction based on multinomial probability model. To the best of our knowledge, this problem has not been addressed yet in the field of recommender system. The prediction of a movie’s genre has many practical applications including complementing the item’s categories given by experts and providing a surprise effect in the recommendations given to a user. We employ mulitnomial event model to estimate a likelihood of a movie given genre and the Bayes rule to evaluate the posterior probability of a genre given a movie. Experiments with the MovieLens dataset validate our approach. We achieved 70% prediction rate using only 15% of the whole set for training.

Index Terms—Recommender system, category prediction, multinomial model, Naïve Bayes classifier.

The authors are with Korea University of Technology and Education 1600, Chungjeol-ro, Byeongcheon-myeon, Dongnam-gu, Cheonan-si, Chungcheongnam-do 31253, Republic of Korea (e-mail: lensky@koreatech.ac.kr, earnaudmakita@gmail.com).


Cite: Eric Arnaud Makita Makita and Artem Lenskiy, "A Multinomial Probabilistic Model for Movie Genre Predictions," International Journal of Machine Learning and Computing vol.6, no. 2, pp. 97-100, 2016.

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

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