A Multinomial Probabilistic Model for Movie Genre Predictions
Eric Makita, Artem A. Lenskiy · International Journal of Machine Learning and Computing · 2016
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.