## 2 Materials and Methods
### Implementation
#### 2.3.4 Recommender
We believe that these enhancements in our model will allow for more dynamic and adaptable user profiling, effectively addressing the broad spectrum of user interests and their evolution over time. Figure 23: Precision@K Performance of NMF and DNN with 900 Users under the Conditions of 50 Topics and 45 Personas. Figure 22: Precision@K Performance of NMF and DNN with 500 Users under the Conditions of 50 Topics and 45 Personas. ## 5 Conclusions

We demonstrated the movies recommendation system based on user personas when a new user is added. However, it is essential to consider the situation when new movies are added. In addition to user personas, creating movie personas presents a novel approach. Movie personas are based on metadata, such as genre, director, cast, and release year. Each movie possesses a unique persona, enabling more sophisticated analysis for movie recommendations. Establishing a model that connects movie personas with user personas makes it possible to identify the movie personas preferred by users and recommend new movies accordingly. For instance, if a specific user persona exhibits a high preference for a particular movie persona, recommending other movies with similar personas becomes feasible. This approach significantly enhances the adaptability to newly added movies. Such an approach finds applications in various domains beyond movie recommendation services. For instance, creating personas for exhibitions or tourist attractions and linking them with user personas is achievable. This enables recommending the most suitable exhibitions or tourist attractions to users by aligning their preferences with the characteristics of the attractions, leading to increased user satisfaction and improved service quality. When the input format remains the same, our method can be applied to various models. For example, if the previous model used was NMF, it can be replaced with a deep learning model using the same input data. This allows leveraging the strengths of each model and integrating results from different algorithms to achieve more precise predictive capabilities. This approach reminds one of the principles of ensemble learning, which combines multiple models to achieve better performance by allowing each model to approach the given problem from different perspectives. Consequently, using a combination of different models through replacement or ensemble methods can enhance insights and accuracy that were not attainable with a single model alone. This approach proves to be highly useful in improving the performance of recommender systems and can also be applied in the research and development of new recommendation algorithms. In this study, a recommendation algorithm was implemented, generating user personas based on user-rating-movie data to recommend movies to users. Figure 24 illustrates the overall configuration of a user persona and movie persona recommender system. In the future, a movie-centric user recommendation algorithm can be implemented by creating movie personas based on movie-rating-user data. Moreover, we can employ ensemble models such as NMF, DL, Graph Convolutional Network (GCN), among others, to link these two algorithms to constitute the recommendation algorithm. The topics of Artificial Neural Networks (AN) and Deep Reinforcement Learning (DRL) are not covered within the scope of this paper. Future research endeavors are planned to extend into the realm of Generative Adversarial Networks (GANs), aimed at processing unstructured data such as images and videos, not just text. Moreover, advancements in accuracy and adaptability will be explored through the application of DRL, which will be a subject of subsequent investigation

Figure 24: The overall Configuration of the Persona Recommender System. **Author Contributions:** Conceptualization; methodology; formal analysis; data curation; writing--original draft preparation; writing--review and editing, H.-C.L., Y.-S.K. and S.-W.K. All authors have read and agreed to the published version of the manuscript.