# Research and Application of Edge Computing and Deep Learning in a Recommender System
## 4 Experimental Process and Experimental Result Analysis
### Experimental Results and Analysis  
A change in the temperature reflects the degree to which the student model pays attention to negative labels. When the temperature is low, the information carried by negative categories is relatively reduced, resulting in less attention to negative categories. A lower probability of negative categories corresponds to reduced attention. When the temperature is high, the probability value of negative categories will relatively increase, and the information carried by negative categories will be relatively amplified. The student model will pay more attention to negative labels. This study selected the temperature [1; 3; 7] for a comparative analysis, and separately trained these student models with different temperature, \(T\), values under fixed samples. The experimental results revealed that when the temperature, \(T\), was taken as 1, 3, and 7, the average inference precision of the student model was 43.2%, 52.8%, and 48.7%, respectively. Therefore, we chose 3 as the temperature parameter.  
After training with the proposed KD model and other algorithms, including FM, FFM, Wide&Deep, DeepFM, and the DCN, a train number recommendation list was generated and compared with the actual purchases made by passengers. The model's performance was then evaluated based on precision, recall rates, and F1 scores. The comparison results are presented in the figures below.  
Evidently, the recommendation results of the five algorithms tested exhibit varying indices. As shown in Figures 3-5, the deep learning-based recommendation model outperforms other models like FM and FFM in terms of precision, recall rates, and F1 scores. Particularly in F1 scores, the proposed KD recommendation model demonstrates certain advantages among the evaluated recommender systems but still lags behind deep learning models, which is especially the case for the DCN.  
Figure 3: Recall comparison.  
The testing results of NDCG are given in Table 2. It can be observed that the deep learning-based recommendation model outperforms other models such as FM and FFM in NDCG. Among them, the DCN performed the best, followed by KD.  
Concurrency testing results are illustrated in Figure 6. Upon the deployment of Wide&Deep, DeepFM, the DCN, and KD models on identical cloud and terminal devices, the KD model exhibits significantly superior dynamic computing time and concurrency compared to other models, including Wide&Deep and DeepFM. Moreover, the cost of model deployment is notably reduced.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **NDCG@4** & **NDCG@8** & **NDCG@12** & **NDCG@16** & **NDCG@20** \\ \hline FM & 0.0379 & 0.0571 & 0.0728 & 0.0531 & 0.0312 \\ \hline FFM & 0.0631 & 0.8391 & 0.1257 & 0.0452 & 0.0429 \\ \hline Wide\&Deep & 0.0931 & 0.1231 & 0.1498 & 0.0943 & 0.0782 \\ \hline DeepFM & 0.1432 & 0.1674 & 0.1982 & 0.1321 & 0.0912 \\ \hline DCN & 0.1627 & 0.2027 & 0.2219 & 0.1337 & 0.0937 \\ \hline KD & 0.1523 & 0.1841 & 0.2087 & 0.1186 & 0.0821 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The NDCG comparison.  
Figure 4: Precision comparison.  
Figure 5: F1 comparison.  
In summary, while the accuracy of the KD model experiences a moderate decrease, the model deployment cost is significantly reduced. Simultaneously, concurrency and the efficiency of real-time computing are enhanced.