## 2 Materials and Methods
### Theoretical Backgrou
#### 2.1.2 Algorithm-Based and Deep Learning Recommender Systems  
Algorithm-based Recommender Systems  
Representative recommender systems include collaborative filtering, content-based filtering, hybrid filtering, etc. Open-source libraries implemented with matrix factorization, a way of implementing collaborative filtering, are available. However, there are three main issues with using such methods to build real-time personalized recommender systems [13]. First, memory usage increases sharply, and the learning speed slows down as the amount of training data increases. Second, user information is utilized not just as a single value of ID but in various ways. The accuracy is high when there exists a large database of user evaluation history, but there is a limitation in utilizing diverse user information. Lastly, it is difficult to implement quick recommendations in real time.  
The advantage of deep learning-based recommender systems is their ability to learn nonlinear and complex relationships among various features (e.g., user, item). While conventional methods focus on the linear relationships between users and items, deep learning uses nonlinear activation functions such as ReLU or Tanh, allowing it to represent nonlinear interactions and model complex relationships among various features. It effectively performs sophisticated inference by capturing various characteristics and interactions of the data, thus learning nonlinear patterns and demonstrating superior performance in various fields. Deep learning is receiving attention in various fields such as computer vision, speech recognition, and natural language processing due to its flexibility and powerful representational capability, contributing significantly to providing innovative solutions. Additionally, deep learning models have a modular expansion structure, ensuring exceptional scalability and flexibility. This implies that they may be combined with and replaced by other models. For instance, various factors such as a user's past behavior, personal characteristics of the user, and the characteristics of items must be considered in recommender systems. The entire system can effectively handle various elements by choosing the deep learning module most suitable for modeling each of these elements and combining them.  
For example, Recurrent Neural Networks (RNN) may be effective in modeling a user's past behavior. At the same time, a Multilayer Perceptron (MLP) can model personal characteristics of the user, and a Convolutional Neural Network (CNN) can be useful for modeling the characteristics of items. By selecting and combining each of these modules, the entire system can effectively handle each element while maintaining flexibility.  
Deep learning-based recommender systems can easily change to new deep learning models. In other words, the existing modules can be easily replaced with new ones when new models appear. This ensures that the recommender system always utilizes the latest technology to provide the best performance. A suitable form of deep learning models can be used to construct a recommender system depending on the form of the data.