  Movie ratings play an important role both in determining the likelihood of a
potential viewer to watch the movie and in reflecting the current viewer
satisfaction with the movie. They are available in several sources like the
television guide, best-selling reference books, newspaper columns, and
television programs. Furthermore, movie ratings are crucial for recommendation
engines that track the behavior of all users and utilize the information to
suggest items they might like. Movie ratings in most cases, thus, provide
information that might be more important than movie feature-based data. It is
intuitively appealing that information about the viewing preferences in movie
genres is sufficient for predicting a genre of an unlabeled movie. In order to
predict movie genres, we treat ratings as a feature vector, apply the Bernoulli
event model to estimate the likelihood of a movies given genre, and evaluate
the 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 for
the task of predicting movie genres. In our approach we attempted to answer the
question: "Given the set of users who watched a movie, is it possible to
predict the genre of a movie based on its ratings?" Our simulation results with
MovieLens 100k data demonstrated the efficiency and accuracy of our proposed
technique, achieving 59% prediction rate for exact prediction and 69% when
including correlated genres.
