# Deep Learning-Based Context-Aware Recommender System Considering Contextual Features
## 1 Introduction  
A recommender system is used to give recommendations to allow users who desire information to quickly find such information when given lots of information. When e-commerce services were newly emerging, users had to invest a significant amount of time to find what they wanted from lists of products or services. However, as recommender systems became available, and users found the technology very useful, and the services were able to increase their revenues. At present, recommender systems are adopted by e-commerce services in various fields such as Netflix, Amazon, and YouTube. Many ongoing researches are exploring how to apply them to each field [1; 2; 3]. Before smartphones became widely available, recommender systems were mainly applied to accessed on desktop computers. They operated based upon basic information--users' purchase history or evaluation scores--and in the early days, recommender systems were modeled by focusing only on users and items only. Considering that contextual factors can impact users' preferences when they select an item, such information can further improve the performance of such systems. For instance, if users watch a movie alone, then it is acceptable to apply the existing recommender system, but if they watch it with their kids, then it is more likely that they will choose to watch G-rated or animated movies in the future. When recommending music to users at home, focus can be placed on either the latest music or their favorite music. However, if users listen to music when they are near the sea, then exciting music or music related to the sea could be recommended. As mentioned earlier, depending on whom they are with or where they are, their preference can be changed. This is called a context-aware recommender system [4], and it is assumed that users' specific context can affect their preferences. At present, contextual information can be collected from users' smartphones or Internet of Things (IoT). The main types ofcontextual information that are used include time, weather, and place. Depending on the recommended content, traffic conditions or companion information can be also used [5; 6]. Categories like weather and location are referred to as contextual dimensions, and information such as sunny, rainy, or gloomy weather, or presence at home or a movie theatre, are called contextual conditions [7]. For a context-aware recommender system, we can use smartphones or IoT to gather various contextual information, but the more contextual information we consider to predict preferences, the more severe data sparsity becomes. In addition, if there is no data regarding a user's context, a context-aware recommender system makes a recommendation based on similar users' contextual data. However, it is difficult to find reliable similar users in the case of data sparsity. The method to solve this problem is matrix factorization (MF), which shows high performance in cases of sparse data [8; 9; 10; 11; 12]. MF finds the factors that are assumed to potentially exist in the data based on the latent factor model. It is highly effective for predicting empty values that are not evaluated in the user/Item matrix. Due to recent exponential increases in the amount of data that can be utilized, as well as with rapid developments in PC hardware, deep learning has been applied to various fields with excellent results [13; 14; 15; 16]. Several ongoing researches are exploring how to apply deep learning to recommender systems. More specifically, some researches are exploring how to improve performance by integrating the main feature of existing recommender systems, collaborative filtering, with deep learning [17; 18; 19]. A deep learning-based recommender system has successfully improved an existing recommender system's performance and accuracy by extracting meaningful features from various data, such as images and review texts [20; 21]. However, there is not yet sufficient research on a deep learning-based context-aware recommender system.