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.