Sequential Movie Genre Prediction Using Average Transition Probability with Clustering

Abstract

In recent movie recommendations, one of the most important issues is to predict the user’s sequential behavior to be able to suggest the next movie to watch. However, capturing such sequential behavior is not easy because each user’s short-term or long-term behavior must be taken into account. For this reason, many research results show that the performance of recommending a specific movie is not good in a sequential recommendation. In this paper, we propose a cluster-based method for classifying users with similar movie purchase patterns and a movie genre prediction algorithm rather than the movie itself considering their short-term and long-term behaviors. The movie genre prediction does not recommend a specific movie, but it predicts the genre for the next movie to watch in consideration of each user’s preference for the movie genre based on the genre included in the movie. Using this, it will be possible to provide appropriate guidelines for recommending movies including the genres to users who tend to prefer a specific genre. In particular, in this study, users with similar genre preferences are organized into clusters to recommend genres. For clusters that do not have relatively specific tendencies, genre prediction is performed by appropriately trimming genres that are not necessary for recommendation in order to improve performance. We evaluate our method on well-known movie data sets and qualitatively determine that it captures personalized dynamics and is able to make meaningful recommendations.

Keywords

• movie genre prediction
• sequential recommendation
• transition probability
• user clustering