# Sequential Movie Genre Prediction Using Average Transition Probability with Clustering
## 1 Introduction  
One of the most important parts of a recommendation system is to model the interactions between users and items as well as the relationships amongst the items themselves [1]. The former is usually called item-to-user recommendation and the latter is called item-to-item recommendation. In a sequential recommendation, user preferences and sequential patterns can be extracted based on these two kinds of interactions. However, learning the personalized sequential behavior from collaborative data is not an easy task since the long-term and short-term dynamics of the users have to be carefully considered for both personalization and sequential transitions. In particular, if data are sparse, estimating the parameters of learning models becomes difficult. For this reason, the main objective is to design personalized models of user behavior using user purchase histories for sequential recommendation systems [2]. In determining the short-term dynamics of a user's behavior, approaches based on the Markov chain (MC) assume that the next action depends on only the previous action. Since the last item is, in general, the key factor affecting the user's next action, first-order MC-based methods show strong performance, especially on sparse data sets [3]. Recurrent neural networks (RNNs) have been used for the long-term dynamics of user behavior. RNNs model all previous actions with a hidden state, which is then used to predict the next action [4]. Both approaches, while strong in specific cases, are limited to certain types of data. MC-based approaches work well with high sparsity settings, but are not as successful in determining the intricate dynamics of long-term complex scenarios. In contrast, RNNs work well in long-term scenarios, which require large amounts of data. Recently, session-based recommendation systems (SBRSs) have been investigated [5] as a new approach in recommendation systems. Distinct from other collaborative-filtering (CF)-based recommendation systems, SBRSs usually model long-term, static user preferences. SBRSs try to capture the short-term, dynamic user preferences for more sensitive and accurate recommendations during the evolution of session contexts. Unlike purchasing one specific item, the authors in [6] considered a sequential recommendation scenario in which users purchase similar items simultaneously. In this scenario, the main objective of the recommendation is to propose a personalized \"list of items\" to each user. Similar to this approach, attribute prediction for movie data can also be considered. For instance, the genre/category of a movie can be an important attribute of user/item similarity in recommendation systems. This information is provided when new content is created. Based on this, the authors in [7] consider a movie recommendation system, based on genre correlations, to improve the existing genre correlation algorithm, and compare the results obtained with those of the previous algorithm. In [8], the authors introduce a recommendation system using movie genre similarity and preferred genres. However, in these studies, only a static situation is considered, not a sequentially changing recommendation system. In this paper, we consider the prediction of movie genres included in preferred movies before recommending movies (See Figure 1). The genre is one of the important features of a movie, which gives guidelines on which movies each user prefers. In the sequential movie recommendation system, we extracted the genre included in the movie each user watched, studied the genre preference, and conducted a study on what movie genre the user will see next. Our genre prediction algorithm does not predict just one genre preferred by each user but multiple genres simultaneously. Hence, it will possible to recommend movies containing several genres so that it will be used appropriately by mixing some existing movie recommendation techniques.