  In recent movie recommendations, predicting the user's sequential behavior
and suggesting the next movie to watch is one of the most important issues.
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 very high 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. Through this, it is
possible to provide appropriate guidelines for recommending movies including
the genre to users who tend to prefer a specific genre. In particular, in this
paper, users with similar genre preferences are organized into clusters to
recommend genres, and in 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 datasets, and qualitatively that it
captures personalized dynamics and is able to make meaningful recommendations.
