# Deep Learning-Based Context-Aware Recommender System Considering Contextual Features
## 2 Related Works  
Earlier, it was mentioned that a context-aware recommender system requires various type of contextual information. In the past, date and time information were mainly used because of their easy availability. Nowadays, it is possible to gather contextual information, such as place, time, and mood, from various users via IoT device sensors or smartphones. As the amount of contextual information to consider increases, it becomes possible to provide more detailed recommendations. At the same time, users are required to provide detailed contextual data, and it is highly unlikely that they will evaluate every item in every context. For this reason, in most cases, contextual data are sparse. To reduce the data sparsity problem, we can use matrix factorization, a latent factor model based collaborative filtering [22; 23]. matrix factorization can be used effectively to predict that scores of items not evaluated by users by finding the latent factors that exist between users and items. Since matrix including contextual data are multidimensional, tensor factorization (TF) is possible [24; 25]. Although TF shows better performance than MF, due to its high complexity, one can apply the weights of contextual features and maintain the user/item matrix. In this model, multidimensional data, including context are expressed in a matrix. Item splitting, in which an item and the contextual condition are handled as one fictious item, is used to create a matrix [26; 27]. However, there are many items and many contextual conditions for each item. Thus, due to complexity and data sparsity in the item splitting process, not every context is reflected. Factorization machine (FM) is more suitable when the dataset has many features, such as tags and genres [28]. FM can be used to insert many features into a model and then model the correlation among variables. Although it shows high performance even with scarce data, it has only one latent vector for one feature. Thus, assuming there are latent vectors A, B, and C, it is unable to distinguish between the impacts of A and B or A and C.  
As deep learning has become more popular, researchers have begun to successfully apply deep learning models to recommender systems and utilize various data types to improve performance. NN applied Neural Collaborative Filtering is simple and effective network. To model interactions between users and items, a dual neural network has been constructed [29]. NCF is characterized by the use of a latent vector obtained by applying MF to user/item matrix. Autoencoder is an unsupervised learning algorithm with no label. The model is run to reduce the input data to optimal dimensions and restore the original data. The input and output of the model dimensions are same. An autoencoder's hidden layer is expressed by the characteristics of input data. The autoencoder is used for matrix completion, as it can fill empty data (i.e., those that used to be null) within the recommender system based on user/item characteristics. In addition, I-AutoRec and U-AutoRec can restore original inputs by using user/item rating as inputs [30]. Recently, there has been an increase in research on deep learning recommender systems that improve accuracy is improved by reflecting the impacts on the user/item using the additional information. Deep learning is useful for processing unstructured data such as text review data or image data. Compared to the existing recommender system, it is possible to handle a greater variety of data and assess the complex relationships among such data. However, there is a lack of current research on the characteristics of contextual information in the context-aware recommender system. In the past, recommendations were based on the impacts of context on items or users. In some cases, the ratings evaluated by users in a specific context are densely located near a certain rating. It can be high or low in the users' preference, depending on the other contexts. This means that there may be correlation between contextual condition.