# A CNN-Based Advertisement Recommendation through Real-Time User Face Recognition
## 2 Related Work  
### Deep Learning-Based Recommender Systems  
Recommender systems are information-filtering systems that can solve the information overload problem by filtering important pieces of information among information generated according to a user's interests, preferences, or observed behavior for a specific item [19; 20]. The information overload problem is a phenomenon in which it becomes more difficult for individuals to make good decisions as the amount of data increases [21]. With the rapid spread of Internet use between the early and mid-1990s, recommender systems based on CF were developed to estimate which of the many items users would like; thus, the problem of information overload was solved [22; 23]. The objective of the recommender system is to make meaningful recommendations from user data for items or products of interest [24]. While customers have more choice, internet shopping malls and service providers face challenges in providing personalized product advertisements to customers. The recommender system collects user preference information for items such as movies, songs, books, travel destinations, and websites. CF is an algorithm that recommends items liked by other users with similar tastes, and word of mouth is implemented as a computer system. CF has been widely used as a recommendation methodology to this day, but it is also prone to the problem of performance degradation owing to data sparsity, as well as cold-start problems, long tail problems, and scalability problems [25; 26]. Deep learning has advanced the structure of recommender systems and provides several ways to improve the performance of recommender systems. The development of deep learning-based recommender systems has received great attention because these systems can overcome the limitations of existing CF-models (e.g., data sparsity problem, cold-start problem, scalability problem, and long tail problem) and achieve high recommendation quality [7; 8; 27; 28; 29; 30]. [PERSON], et al. [31] extracted latent features of user preferences or ratings using restricted Boltzmann machines and an undirected two-layer graphic model as a kind of graphic probabilistic model. [PERSON], et al. [32] extracted high-level features from low-level features for user preferences through a deep belief network, which is a deep neural network composed of multiple layers of latent variables. Auto Encoder, a model of deep learning, is used to reduce the dimensionality of the user item matrix and extract more latent features using the encoder output [33; 34]. [PERSON], et al. [35] analyzed user behavior changes over time using a recurrent neural network (RNN), a deep learning model specialized in processing sequence data, and combined analysis results from RNN and the latent factor regarding user preferences to offer more accurate recommendations. In addition, the CNN, which shows excellent performance in tasks such as image recognition and object classification, extracts latent factors and latent features from raw data such as audio data, text data, and image data, thereby providing good performance for recommender systems [36; 37]. [PERSON], et al. [38] proposed a recommender system to solve a problem that makes it difficult for viewers to find interest anchors. Therefore, the authors developed a multi-head connected device to capture the preferences between anchors and viewers and extract related functions for the expression. The results show that the proposed model outperforms state-of-the-art recommendation models. In summary, deep learning-based recommender systems have been developed to predict a user's preference or recommend items suitable for said user's preference by extracting the hidden meaning between the user and the item from various types of input data such as images and unstructured data, which were previously impossible to analyze.