A movie genre prediction based on Multivariate Bernoulli model and genre correlations
This is an incremental improvement for movie recommendation systems by leveraging ratings data more effectively.
The paper tackled predicting movie genres from user ratings using a Multivariate Bernoulli model and genre correlations, achieving 59% exact prediction accuracy and 69% when including correlated genres on MovieLens 100k data.
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.