## 2 Materials and Methods
### Implementation
#### 2.3.4 Recommender

Table 7: Precision@k Metrics—MSEs of NMF and DL were 0.976 and 0.837, respectively without Persona, while with Persona, they were 0.755 and 0.805. \begin{table}
\begin{tabular}{c c c c c} \hline  & **Persona Not Applied** & \multicolumn{2}{c}{**Persona Applied**} \\ \hline  & **NMF** & **DL** & **NMF** & **DL** \\ \hline Recommended time (ms) & 22 & 44.8 & 23 & 44.9 \\ \hline \end{tabular}
\end{table} 
Table 8: Time to recommend 1000 movies by model Recommendation times for NMF and DL were 22 ms and 44.9 ms, respectively, without Persona, while with Persona, they were 23 ms and 44.9 ms.

of Persons * number of movies\" information is generated, which can effectively search and learn data. Also, because the number of Personas is fixed, there is no need to update the model by performing learning even if users are added. [Table 9] shows a comparison of matrix sizes between when 60 Personas are applied and when not applied. A matrix of information for 5000 users and 10,000 movies (5000 \(\times\) 10,000) is generated, and a matrix for 10,000 users and 20,000 movies (10,000 \(\times\) 20,000) is generated. When applying 60 Personas, the matrix sizes of 5000 users (60 \(\times\) 10,000) and 10,000 users (60 \(\times\) 20,000) can be compared. ## 4 Discussion

Through topic modeling using LDA, the recommender system condensed the complex preferences, interests, and favored characteristics of individual users to a lower-dimensional representation; then, the system captured the essence and tendencies of users' inherent characteristics by completing user personas through k-means clustering based on the results of user topic modeling. We elucidate the methodologies implemented to mitigate the cold start issue. Intentional data input through multi-stage recommendations or tournament-style selections, encouraging users to input their preferences. This allows the system to discern user preferences and construct a persona, which is then used to recommend items corresponding to that persona. Prompting users to create an initial profile at sign-up, which serves as a foundational persona for personalized recommendations. Generation of provisional user data by assigning popular items or highly rated items as initial data for new users. Creation of hybrid user data by combining strategies (1) and (3), thus forming a more robust initial user data set. The essence of the cold start problem lies in the lack of data. To tackle this, we have devised a system where new users are encouraged to fill out initial profiles upon entry, which are then made to be persona. This approach fundamentally resolves the data scarcity issue inherent to the cold start problem, as it allows the system to generate relevant recommendations even for users without historical data. With user personas, NMF and DL models were implemented to create a movie recommendation service. A comparison of the movie recommendation results was made between NMF and DL models, with and without the use of user personas, using NMF models without personas as the baseline. The results showed that the use of user personas enabled faster recommendations even with an increasing number of users but with a relatively lower precision. A performance improvement in the NMF and DL models with user personas was required. To evaluate the models, we used MSE as a metric, and the NMF model with personas demonstrated lower performance compared to the NMF model without personas in terms of @Precision Top 10, with scores of 94.16% and 92.27%, respectively. As N increased (e.g., Top 50), the performance gap between the two models widened (N-Persona 90.99%, Persona 67.36%). This change is from transforming the relationship between users and ratings to the relationship between personas and ratings. For example, assuming 100 users and that each user has a persona value of 20, the data for 100 users can be compressed to 20. However, obtaining precise recommendations for