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General Information
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
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.

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