## 2 Materials and Methods
### Implementation
#### 2.3.4 Recommender
At this stage, the model recommends the top 5 movies excluding the movies the user has already watched. 3 The user watches the top N movies and provides the ratings for the movies as feedback to the recommender system. 4 The recommender system stores the user's movie rating information in the database and sends it to the Persona update module. 5 The Persona update module adds the added movie rating information to the existing user corpus, performs the preprocessing procedure, and checks if the Persona has changed after calculating the user Persona. If the Persona has changed, the movie is recommended based on the changed Persona information in step 2. This process is repeated arbitrarily 10 times to accurately update the Persona through repetitive learning. The more movie recommendations are made, the more movies of the genre that match the user's preferences are recommended, and topic is updated accordingly. Ultimately, the change of the topic means that the individual's preference is changing, which leads to the change of the user Persona. The initial user Persona is 12 and the topics are Topic 9, 16, and 19 respectively, and the topic contributions are 2, 5, and 3. After watching 35 recommended movies, the user Persona changed from 12 to 22. After the 45 th movie was recommended, the Persona changed back to 12. This shows that the user's latent preferences have changed. The recommended genre confirmed that many representative genre movies were recommended, with Drama increasing from 31 to 55 and Thriller from 19 to 31. [Table 3] presents the recommendation results when the Persona changed due to movie recommendations. Figure 19: Movie Recommendation Process according to Changes in User Preferences. ### Evaluation

(1) Evaluation of the Model by Recommended Data

One method for evaluating recommendation models is by computing Precision at K. Precision at K is used when the predicted value is numerical data, with a Threshold value applied to distinguish values equal to or higher than that value as Relevant items and values lower than that value as irrelevant items, to calculate Precision [13]. Here, K represents the top recommended items, or the data with the highest predicted value. The Precision at K method determines relevant and recommended items as shown in [Table 4]. Then, Precision at K is calculated, as shown in [Table 5]. Precision at K indicates the proportion of recommended items in the relevant Top-K set. This study set the Threshold value at 3.5 and increased @K from 10 up to 50 in increments of 10 to calculate and evaluate the Precision at K and Hit at K for the NMF and Deep Learning models. The evaluation results showed that the performance of both models decreased as Top K increased. In particular, the rate of performance decrease for the NMF model was higher from Top 30 onwards. Typically, recommender systems focus on finding the top few items related to a user's preferences. Therefore, the top items in the recommendation list reflect the user's preferences the best, resulting in the highest values for Precision at K and Hit at K for the top K recommendations. However, if the value of K is increased, or more items are included in the recommendation list, it is likely that adding items that do not reflect the user's preferences well increases. This implies that recommended items located in the lower parts of the list are less likely to match the user's preferences. Therefore, as the value of K increases, or the recommendation list is made longer, the values of Precision at K and Hit at K may tend to decrease. Especially, the NMF model, being a matrix factorization-based model, might be less effective in capturing complex user behavior or interactions. This implies a relatively faster decrease in performance compared to the Deep Learning model. This feature may account for the faster performance decrease observed in the NMF model from Top 30 onwards. [Table 6] shows the @K metrics of the NMF and DL models.