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Movie ratings play an important role both in determining the likelihood of apotential viewer to watch the movie and in reflecting the current viewersatisfaction with the movie. They are available in several sources like thetelevision guide, best-selling reference books, newspaper columns, andtelevision programs. Furthermore, movie ratings are crucial for recommendationengines that track the behavior of all users and utilize the information tosuggest items they might like. Movie ratings in most cases, thus, provideinformation that might be more important than movie feature-based data. It isintuitively appealing that information about the viewing preferences in moviegenres is sufficient for predicting a genre of an unlabeled movie. In order topredict movie genres, we treat ratings as a feature vector, apply the Bernoullievent model to estimate the likelihood of a movies given genre, and evaluatethe posterior probability of the genre of a given movie using the Bayes rule.The goal of the proposed technique is to efficiently use the movie ratings forthe task of predicting movie genres. In our approach we attempted to answer thequestion: "Given the set of users who watched a movie, is it possible topredict the genre of a movie based on its ratings?" Our simulation results withMovieLens 100k data demonstrated the efficiency and accuracy of our proposedtechnique, achieving 59% prediction rate for exact prediction and 69% whenincluding correlated genres.

A movie genre prediction based on Multivariate Bernoulli model and genre correlations