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In recent movie recommendations, predicting the user's sequential behaviorand suggesting the next movie to watch is one of the most important issues.However, capturing such sequential behavior is not easy because each user'sshort-term or long-term behavior must be taken into account. For this reason,many research results show that the performance of recommending a specificmovie is not very high in a sequential recommendation. In this paper, wepropose a cluster-based method for classifying users with similar moviepurchase patterns and a movie genre prediction algorithm rather than the movieitself considering their short-term and long-term behaviors. The movie genreprediction does not recommend a specific movie, but it predicts the genre forthe next movie to watch in consideration of each user's preference for themovie genre based on the genre included in the movie. Through this, it ispossible to provide appropriate guidelines for recommending movies includingthe genre to users who tend to prefer a specific genre. In particular, in thispaper, users with similar genre preferences are organized into clusters torecommend genres, and in clusters that do not have relatively specifictendencies, genre prediction is performed by appropriately trimming genres thatare not necessary for recommendation in order to improve performance. Weevaluate our method on well-known movie datasets, and qualitatively that itcaptures personalized dynamics and is able to make meaningful recommendations.

Sequential Movie Genre Prediction using Average Transition Probability with Clustering