# Dynamic Personalized Recipe Recommendations Based on Facial Health Recognition
## 4 System Evaluation
### Analysis of Experimental Results  
To verify the superiority of the hybrid recommendation algorithm based on content and collaborative filtering used in this experiment, a comparative experiment was conducted between the hybrid recommendation algorithm, the user-based collaborative filtering algorithm [20], and the item-based collaborative filtering algorithm [21]. The effectiveness of the given framework in health status model training, recipe feature extraction, and user demand analysis are calculated respectively. These data provide us with a wealth of information that helps us better understand and apply knowledge in the areas of recipes and health.  
The recall rate indicates the proportion of items that the user likes or is interested in among the TOP-N recommendation results, compared to all the items that the user likes. The calculation formula is as follows:  
\[\text{Recall}=\frac{\Sigma_{\text{u}\in\text{U}}|\text{R}(\text{u})\cap\text{ T}(\text{u})|}{\Sigma_{\text{u}\in\text{U}}|\text{T}(\text{u})|} \tag{15}\]  
The coverage rate refers to the proportion of the TOP-N recommended results by the system to the entire recommended project set. This indicator reflects the recommendation system's ability to mine long-tail projects from the side. The calculation formula is as follows:  
\[\text{Coverage}=\frac{|\text{U}_{\text{uffll}}\text{R}(\text{u})|}{\text{I}} \tag{16}\]  
Here, the total number of items is depicted.  
When the recommended length N is 5, 10, 15, 20, 25, and 30, the accuracy, recall, and coverage rates of the three methods are shown in Tables 1-3. We then draw the change trend when the recommended length is 5, 10, 15, 20, 25, and 30, as shown in Figure 7a,c.  
As can be seen from Table 1 and Figure 7a, the hybrid recommendation algorithm based on content and collaborative filtering used in this experiment has better accuracy than the user-based and item-based collaborative filtering algorithms in the listed situations. When the recommended length is greater than or equal to 25, the accuracy gradually tends to stabilize.  
\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 & 64.25 & 52.78 & 50.86 & 43.77 & 36.94 & 35.14 \\ User-based & 46.22 & 40.50 & 31.98 & 28.97 & 25.41 & 23.42 \\ Item-based & 39.64 & 32.43 & 29.21 & 24.26 & 22.12 & 21.13 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Accuracy.  
As can be seen from Table 2 and Figure 7, the recall rate of the hybrid recommendation algorithm used in this experiment gradually outperforms the user-based and item-based recommendation algorithms as the length of the recommendation queue increases. When the length reaches 15, the recall rate of the hybrid recommendation algorithm starts to be greater than that of the item-based recommendation algorithm.  
As can be seen from Table 3 and Figure 7, the coverage rate of the hybrid recommendation algorithm used in this experiment is also better than the other two algorithms. When the recommended length is greater than or equal to 25, the coverage rate gradually tends to get stabilized.  
In summary, the hybrid recommendation algorithm based on content and collaborative filtering used in this experiment can not only solve the cold start problem, but also performs better than the traditional recommendation algorithm based on collaborative filtering. Moreover, it can better provide recommendation services to users.