# Dynamic Personalized Recipe Recommendations Based on Facial Health Recognition
## 4 System Evaluation
### User Satisfaction Research  
In practical applications, the most accurate recommendation is not necessarily the most suitable recommendation for the user. As mentioned above, the purpose of our personalized recipe recommendation system based on the user's physical health status is to make the recommendation results more suitable for the user, rather than blindly catering to the user's preferences. Therefore, the advantages of our design cannot be achieved through the widely used methods mentioned above. To address this issue, we conducted a user satisfaction study and compared the performance of the following three recommendation methods:  
1. [label=(R0)]
2. Completely random recipes.
3. When using our HFRR model to recommend recipes, the suitability factors will be set based on the user's current health.
4. When using our HFRR model to recommend recipes, suitability factors will be set according to user-defined needs.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **Top-5** & **Top-10** & **Top-15** & **Top-20** & **Top-25** & **Top-30** \\ \hline Mixed & 25.35 & 27.49 & 37.46 & 39.76 & 46.54 & 46.94 \\ User-based & 19.72 & 23.50 & 31.28 & 32.06 & 36.11 & 37.20 \\ Item-based & 27.44 & 32.30 & 35.81 & 37.36 & 37.89 & 39.00 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Recall.  
Figure 7: Recommended Indicator. (**a**) Precision. (**b**) Recall. (**c**) Coverage.  
The 50 volunteers in Section 4.1 also participated in the study, all of whom were university students with an average age of 21 years, of whom 28 were male and 22 were female. According to the users' dietary preferences, physical health status, and demand for personalized recipes, we divided them into experimental groups and control groups. Thirty of them were in the experimental group and the other twenty were in the control group. They did not know which group they were in. Users in the experimental group are recommended by R2 and R3, while users in the control group are recommended by R1.  
In each test, users were asked to receive three recipe recommendations and fill out a six-question questionnaire (see Table 4 below). First, we asked participants about their overall satisfaction (Q1). Then, we measured user satisfaction (Q2 to Q4) in detail based on the components of quality, practicality, and dietary preferences. Finally, we asked specific questions about the health benefits of personalized recipes, which included measurements of current health status (Q5) and health-improving effects (Q6).  
The questions mentioned in the table above were evaluated using a 5-point Likert scale. The results are shown in Figure 8. From Figure Q6, R2, and R3, we can observe the obvious advantages over R1, which means that the recipe recommendations provided by R2 and R3 have the function of improving the users' health statuses. Moreover, we can see that the results of R2 and R3 are also significantly better than R1 in terms of satisfaction and emotion measurement (Q1 to Q5). In terms of overall satisfaction and practicality, the R3 with user-defined settings performs better. This shows that suitability factors based on user-defined settings work better in the recommendations, but also shows that it is difficult to capture the differences in individual needs between each individual.