## 1 Introduction
The reason for repeatedly calculating the similarity for new users is to compare the preferences of the new users with those of the existing users to optimally recommend. This enables fast recommendations difficult due to the time and resources consumed by the iterative calculations required to compare the preferences of new users or items with those of existing users [1]. The need to calculate user similarities for a recommender system to reflect user preferences and provide personalized recommendations contributes to this issue. Such similarity calculations are time and resource-intensive tasks, with computational complexity increasing as the number of users or items increases. The need to recalculate these similarities each time a new user or item is added results in increased data storage and learning and computation time [2]. However, the existing techniques has several challenges. First, as the size of the computational matrix expands, computational time quickly grows. This makes it difficult  
Figure 1: Existing Recommender System—The process of Existing recommending movies with a conventional recommender system. to quickly make recommendations based on new data and results in increased costs. Second, the lack of initial interactions between users and items results in the sparsity, leading to decreased accuracy in recommendations. Third, the Cold Start emerges when new users or items are added to the recommender system. This problem results from the lack of initial information about users or items, making it difficult to make recommendations. Lastly, it is difficult to generate recommendations for inactive users or those not previously included in the recommender system. This study proposes a user persona-based recommendation technique to address these issues. The user persona-based technique was developed to clearly understand and explain users' goals and behavioral patterns [3]. A persona is a virtual character that consumes items and represents actual users, reflecting their characteristics. This technique helps to understand the target user and design products or services that suit that user from a user-centric perspective. The recommender system implemented in this study used the movie rating data in MovieLens [4]. The ratings written by users were topic modeled through Latent Dirichlet Allocation (LDA). Then, each user was assigned topics, and these topics were clustered using K-Means Clustering to complete the final user Persona. Collaborative filtering (CF) is divided into memory-based CF and model-based CF [5]. Memory-based CF calculates the similarity of surrounding users to predict ratings. When the ratings data evaluated by the user is small compared to the total ratings data, the number of common evaluation items among users decreases. This makes it difficult to accurately calculate the similarity between users, leading to a decline in the accuracy of recommendations. Additionally, when new users or rating information is registered in the recommender system, situations in which there are few or no common evaluation items occur. Finally, when there are a small number of common evaluation items among users, the task of calculating the similarity between all users is very inefficient, and the complexity of calculations increases as the number of users and items increases with system expansion. These negatively affect the performances when there are few common evaluation items among users [6]. To overcome the lack of common evaluation items, we used the Non-negative Matrix Factorization (NMF), a model-based Collaborative Filtering (CF) method. NMF works by decomposing the rating matrix into two lower-dimension submatrices and then recombining them to predict missing ratings. Furthermore, we implemented our recommendation algorithm using a Deep Learning algorithm, one of the machine learning algorithms known for its outstanding performance. This method uses a multi-layer artificial neural network to simultaneously perform feature extraction and recommendation modeling.