# Dual-Channel Feature Enhanced Collaborative Filtering Recommendation Algorithm
## 4 Evaluation and Analysis
### Experimental Analysis  
#### 4.2.1 Sensitivity Experiment of Hyperparameter  
(1) The Highest Neighborhood Order in Local Convolutional Channels  
We set the number of neighboring nodes in the global convolutional channel to 20 and the parameter \(\lambda\) value to 0.6. By adjusting the highest neighborhood order in the local convolutional channel, we conducted experiments on three sets of datasets based on evaluation indicators Recall@10 and NDCG@10. The experimental results are shown in Figure 3.  
From Figure 3, it can be seen that the performance of the model gradually improves with the increase in neighborhood order. Except for the MovieLens-100K dataset, it performs best when the highest neighborhood order is three. Therefore, the highest neighborhood order of the local convolutional channel is set to three.  
The Number of Neighboring Nodes in Global Convolutional Channels  
We set the highest neighborhood order of the local convolutional channel to three and the parameter \(\lambda\) value to 0.6. We conducted experiments by adjusting the number of neighboring nodes in the global neighborhood of the global convolutional channel. The experimental results are shown in Figure 4.  
From Figure 4, it can be seen that on the three datasets, the DCFECF model has the best performance for the evaluation indicators Recall@10 when the number of neighboring nodes is 40, 30, and 30, respectively; for evaluation indicators NDCG@10, the best performance is achieved when the values are 40, 20, and 30, respectively. It can be seen that the model performs better when the number of neighboring nodes in the global neighborhood is around 30. Excessive neighborhood expansion may extract unnecessary node information. Therefore, the number of neighboring nodes in the global convolutional channel is set to 30.  
Figure 3: Model performance under different neighborhood orders.  
The Value of Parameter \(\lambda\) in the Time Factor Expression  
We set the highest neighborhood order of the local convolutional channel to 3 and the number of neighboring nodes in the global convolutional channel to 30. We adjusted the value of parameter \(\lambda\) in the time factor expression, and the experimental results are shown in Figure 5.  
From Figure 5, it can be seen that on the three datasets, the DCFECF model exhibits the best performance for the evaluation indicators Recall@10 when parameter \(\lambda\) values are 0.6, 0.6, and 0.6, respectively; for evaluation indicators NDCG@10, the best performance is achieved when \(\lambda\) values are 0.7, 0.6, and 0.6, respectively. It can be seen that when the parameter \(\lambda\) value is around 0.6, the DCFECF model performs better. In addition, the performance of the model is greater when the value of parameter \(\lambda\) is 1 than when the value is 0. From Equation (23), it can be seen that when \(\lambda\) is set to 1, only the time interval factor is considered, and when \(\lambda\) is set to 0, only the rating item density factor is considered. This indicates that when only one factor is considered, the time interval factor has a greater impact on the user's memory level, while the rating item density factor has a smaller impact on the user's memory level. When the value of \(\lambda\) is around 0.6, the coefficient ratio of the time interval factor is higher, while the coefficient ratio of scoring item density factor is lower, which is consistent with the experimental results that the time interval factor has a  
Figure 4: Model performance under different numbers of neighboring nodes in the global neighborhood.  
significant impact on users' memory level. By taking an appropriate value for parameter \(\lambda\) and balancing the coefficient ratio of the two factors, the filtering effect of the time factor on the model prediction results can be better. This indicates that it is reasonable to consider both the time interval factor and the density factor of scoring items and set parameter \(\lambda\) to balance the proportion of the two factors.