## 2 Materials and Methods
### Implementation
#### 2.3.3 Recommender Model Learning  
We implemented recommendation models using NMF and Deep Learning. The Deep Learning model was implemented by referencing the Deep Neural Network for Youtube Recommendations [Figure 13], and consists of a candidate generation model and a ranking model. The candidate generation model extracts N candidates from large amounts of data, while the ranking model recommends based on the N recommendation target data extracted from the candidates. The NMF model uses movie IDs, user personas, and rating information to train the model and create a Recommender model.  
The Deep Learning model generates training data with user profiles, user personas, and the movie information viewed by each user. In addition to this information, age data for each movie is created, adding weight to newer movies. The training data created in the data preprocessing process is then used to train the candidate generation model using the artificial neural network model. The movie data extracted from the candidate extraction model is used to add user personal information and train the ranking model. This is done to narrow down the range of large amounts of data that need to be analyzed using the candidate model with limited information and then recommend more accurately using the ranking model. In this study, the candidate model was not used and the ranking model was modified and used as shown in [Figure 14]. [Figure 14] visualizes the process of receiving a user or system request, recommending a movie to the user using the trained NMF model, and providing feedback.  
Figure 14: User Persona Movie Matrix—visualizes the process of receiving a user or system request, recommending a movie to the user using the trained NMF model, and providing feedback.  
Figure 13: Deep Neural Networks for YouTube Recommendation—Recommendation system architecture demonstrating the “funnel” where candidate videos are retrieved and ranked before presenting only a few to the user.  
The first stage is the data collection stage, which includes the process of continuously collecting data by uploading a video and entering meta-information about the video.  
The second stage is the candidate extraction stage. In this stage,'subnet' videos suitable for the user are extracted from the video by receiving inputs such as YouTube user's viewing information, 'like', and other feedback, and the candidates are formed. The candidates derived at this point are data that are generally considered suitable for users.  
In the ranking decision stage, the data generated in the candidate extraction stage is evaluated and ranked as 'video itself', 'user-centered', or 'diversification'. 'Video itself' evaluates based on the attributes of the video itself, irrelevant to the user, 'user-centered' evaluates based on the user's taste and environment, and 'diversification' includes the process of providing various contents by limiting the provisioning channel of related contents in order to prevent the generation of biased recommendation data with similar specific contents only with the evaluations conducted earlier.  
The last stage is the content provision stage, where the content is provided to the user in order from high to low based on the score determined in the ranking decision stage. This allows for personalized content recommendations that match the preferences of users.