## 1 Introduction
It learns the information of interactions between the user and the item, thereby providing accurate recommendations. We compared the movie recommendation information using the recommender system implemented through these two methods. The results showed that the system using the Deep Learning algorithm demonstrated a higher accuracy and performance [Table 1]. The NMF recommendation algorithm works by extracting latent factors from the user and item matrix. It represents each user and item as an n-dimensional vector and factorizes it into two lower-dimensional vectors. This factorization allows each element of the given user-item matrix to be represented as the dot product of the user vector and the item vector. These factorized vectors are used to recommend new items or analyze user preferences [7]. \begin{table}
\begin{tabular}{c c} \hline
**NMF** & **Deep Learning** \\ \hline Used for rating-based recommender systems & Used not only by ratings, but also by other recommender systems \\ Find latent characteristics between users and items that & Used when there are many abstract and complex characteristics in \\ influence user behavior and ratings & the data. \\ Fast analysis of complex data & Possible to make more precise and better recommendations \\  & through user information and item information \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison between NMF and Deep Learning Recommender Systems. The NMF method is especially effective when there are few features or factors within the user-item matrix. Furthermore, NMF can be easily updated even when new items or users are added, without recalculating the existing data since it extracts latent factors within the data. NMF has a constraint that the factorized matrices are non-negative (0 or above). This prevents predicting user-item preferences below 0, providing more accurate recommendations. Secondly, NMF shows that the factorized matrices represent the latent features of users and items. This allows easy interpretation of what each feature means, enhancing the performance of the recommender system and increasing understanding of user-item interaction. Lastly, it reduces the dimensionality of the data. Since NMF factorizes into a low-rank matrix, it reduces the original high-dimensional data to a lower dimension. This simplifies complex data for faster processing during analysis. Recommender systems using Deep Learning have various features. Firstly, they can utilize not only rating data but also various other data types. Conventional recommender systems primarily used item rating data evaluated by the user. However, recommender systems using Deep Learning can use not only rating data but also various data such as click history, search history, purchase history, review content, and activities on social media. Secondly, they can be used when the data contains many abstract and complex features such as the movie's genre, director, and actors, which are difficult to express in simple numbers or categorical data in a movie recommender system. Deep Learning can easily learn such abstract information. Thirdly, recommender systems using Deep Learning can provide more precise recommendations using various information about users and items. For example, it can recommend movies based on the information about the user's preferred genre, actors, and directors. In programs that do not apply topic modeling and personas, the approximate recommendation time is about 10 s with an accuracy of 10%. However, with our modeling, the time is reduced to about 3 s, and the accuracy increases by 40%. Additionally, our model enables responsive movie recommendations based on changes in user personas. All in all, recommender systems using Deep Learning can use more diverse data, easily learn data with abstract and complex features, and provide more precise and better recommendations, compared to traditional recommender systems.