## 2 Materials and Methods
### Theoretical Backgrou
#### 2.1.7 Deep Learning  
Various single neural network block models can be utilized when building recommender systems based on deep learning. A Deep Neural Network (DNN) is an Artificial Neural Network (ANN) composed of an input layer that receives data from the outside, an output layer that produces the final result, and multiple hidden layers that process the inputs from their preceding layers. This structure mimics the network of neurons in the human brain, having the ability to self-learn and identify patterns in the given data, performing tasks like prediction and classification. DNNs learns large data such as user behavior patterns, preferences, and histories. Other types of deep learning that use single neural networks include MLP (Multi-Layer Perceptron), the most basic form of ANN, consisting of multiple hidden layers and an output layer, with non-linear activation functions used between the input and hidden layers. The Autoencoder (AE) is a unsupervised learning model, which learns the features of data by compressing input data into a latent space and restoring it. In recommender systems, AEs can be used to extract latent features from input data or to preprocess tasks like noise reduction. Convolutional Neural Networks (CNN), a neural network model primarily used in image processing, consists of convolutional and pooling layers. In recommender systems, CNNs can be used to process and extract features from item images or text data. Recurrent Neural Networks (RNN) are specialized for sequential data processing, capable of handling data that changes over time. In recommender systems, RNNs can be used to learn and infer user's time-series behavior patterns. Restricted Boltzmann Machine (RBM), a neural network model for unsupervised learning, consists of input data and hidden units. RBMs learn the distribution of data, and they can model relationships between items in recommender systems through this distribution. Neural Autoregressive Distribution Estimation (NADE), a neural network model for probabilistic modeling, is a type of autoregressive model. NADE models input data sequentially to predict the next value. Adversary Network (AN), a neural network model used in generative modeling, consists of a Generator and a Discriminator. The Generator network creates fake data, while the Discriminator network distinguishes between fake and real data. Such ANs can be used in recommender systems to model user preferences. Attentional Models (AM) are neural network models that focus on processing important parts of the input data, assigning different weights to various parts of the input data for learning. In recommender systems, AMs can be used to discover high-interest areas for users or important characteristics of items. Deep Reinforcement Learning (DRL) is a neural network model for reinforcement learning, where an agent interacts with the environment and learns in a direction that maximizes rewards. In recommender systems, DRL can be used to optimize rewards based on user actions and perform optimal recommendations. These various neural network models are used in deep learning-based recommender systems, and each model can be selected and applied according to specific data characteristics or recommendation goals. This allows for diverse and accurate recommendation provisions [15].