## 4 Discussion  
The evaluation and analysis of the content-based and matrix factorization recommender systems provide insights into their performance and effectiveness in generating recommendations. The content-based method presents a limitation in its evaluation, as it does not have a direct method for evaluating the feasibility of recommendations using well-known metrics such as RMSE and MAE. This limitation requires the evaluation of the model with the help of the user, which can be biased because it is based on personal preferences rather than objective facts. To address this limitation, the evaluation of the content-based recommender system in the current study employed a custom metric. This metric assessed the recommendations' quality by calculating the ratio of the present restaurants among the 30 recommended restaurants. While this approach provides a means of evaluating the content-based method, it is important to consider its limitations due to the reliance on subjective user preferences. On the other hand, the performance of NMF, SVD, and SVD++ has been evaluated in five-fold cross-validations using two standard metrics, RMSE and MAE. The evaluation results demonstrate the effectiveness of SVD in generating accurate recommendations in the context of the current study. However, it is important to note that the performance of collaborative filtering methods may vary depending on the characteristics of the dataset  
Figure 11: The Riyadh restaurants dataset after adding new user data. Figure 12: Recommendation results. and the specific recommendation problem. Therefore, it is recommended to conduct a thorough evaluation and comparison of different algorithms before selecting the most appropriate method. We also compared the performance of our system with the popularity-based recommender system. Our system performed slightly better in terms of accuracy compared to the popularity-based recommender system. The improved performance of our system can be attributed to the collaborative filtering approach, which is able to capture the complex relationships between users and items. The matrix factorization algorithm is able to effectively handle the large and sparse dataset, resulting in more accurate and relevant recommendations. Moreover, the system can be adapted to different types of restaurants and cuisines, making it applicable to a wide range of users. This is in line with the fact that personalized recommender systems have widely used the matrix factorization model due to its high accuracy and scalability, as noted in [16; 39]. The deployment of the content-based and collaborative-based recommender systems in the web application provides a user-friendly interface for generating personalized recommendations based on user preferences. However, the development of the recommender systems presented several challenges, including working with multiple scripting/programming/markup languages and conflicts with Python libraries when working in the localhost and Apache server. To overcome these issues, the development team relied on adding user entries through PHP into an entries log file (content_based.csv). Then, Python read and processed these entries to generate the resulting recommended restaurants in an external text file (content_result.txt), which the system grabbed using PHP, read its values, and reflected them into HTML using JavaScript to manipulate the document object model (DOM) elements of the HTML structure. Furthermore, the team found it easier to work with a virtual environment, such as Jupyter and Google CoLab, to overcome conflicts with Python libraries. One limitation of our system is the reliance on user reviews and ratings, which may not always reflect the true preferences of users. Despite these challenges, the deployment of the recommender systems has practical implications for restaurant owners and users in Riyadh city. The content-based system can provide personalized recommendations to users based on their preferences, improving their dining experiences and increasing customer satisfaction.