# Sequential Movie Genre Prediction Using Average Transition Probability with Clustering
## 2 Related Works  
In recent sequential recommendation systems, most studies have focused on predicting the short-term and long-term preference dynamics of users. The Markov chain approach has been studied as a short-term dynamic approach. [PERSON] et al. [9] described a sequential recommender based on Markov chains. They studied how to capture sequential patterns to predict the next state with a standard predictor such as a decision tree. [PERSON] et al. [6] proposed a factorizing personalized Markov chain (FPMC) to model the history of a basket based on user-specific transitions of a Markov chain. FPMC propagates information among users, items, and transitions favoring similar patterns to extract the sequential pattern. [PERSON] et al. [10] considered a recommendation system based on Markov decision processes (MDPs). For this, they used maximum likelihood estimates (MLE) of MC transition graphs and suggested several heuristic approaches such as clustering and skipping. [PERSON] et al. [11] adopted pattern mining methods to extract sequential patterns for generating recommendations. [PERSON] et al. [3] proposed a translation-based recommendation (TransRec) for sequential data. Their approach considers items (movies) as translation vectors. [PERSON] et al. [12] used MC to recommend courses taken by students. They estimated the transition probability of the MC from the record of courses students take based on MLE and enhanced MLE with skip-gram modeling. [PERSON] et al. [13] considered the temporal dynamics of the evolution of users and items over time-based Netflix data. For the long-term dynamics, most recommendations rely on matrix factorization (MF) or other similarity-based approaches. In previous work [14], the authors used MF to formulate the recommendation problem as a problem that infers missing values from a partially observed user-item matrix. [PERSON] et al. [15] proposed a maximum margin MF, which uses low-norm instead of low-rank factorization. [PERSON] et al. [16] considered a probabilistic MF (PMF) model that expresses the user preference matrix as the product of two lower-rank user and item matrices. The PMF approach is especially effective in making better predictions from sparse user rating data. [PERSON] et al. [1] suggested an extended FPMC, called Fossil, to present the information of sequential patterns by considering high-order Markov chains and similarity models. Factorization machine-based sequential recommendation systems usually utilize matrix or tensor factorization to factorize the observed user-item-related data into latent factors of users and items for recommendations [4]. Specifically, some works [17; 18] have used the estimated latent representations as input of a network to further calculate an interaction score between users and items or successive users' actions. Recently, deep learning technologies, such as RNNs [4; 19; 20], long short-term memory (LSTM) [21; 22], and Gated Recurrent Unit (GRU) [23] have been applied to the sequential recommendation problem. These deep-learning-based recommendation systems have performed particularly well in sequential recommendations. In [4], the authors suggested new ranking loss functions corresponding to RNNs in the recommendation model. In [19], the authors designed a novel recommendation model, based on recurrent collaborative filtering (RCF), which combines RNN and CF. In [20], the authors introduced an algorithm called recurrent translation-based network (RTN). Their model reflects both short-termand long-term user' preferences. In [21], the authors considered LSTM to extract the dependencies of both users and movies. Unlike prior recommendation models, they considered a method of updating the state with recent operations as input. In [22], the authors introduced an LSIC model, leveraging long- and short-term information in content-aware movie recommendation via adversarial training, which combines the global behaviors from MF with RNN for the top-\(N\) movies.