# Dual-Channel Feature Enhanced Collaborative Filtering Recommendation Algorithm
## 4 Evaluation and Analysis
### Experimental Analysis
#### 4.2.3 Ablation Experiment  
(1) The Ablation Experiment on the Method of Calculating Transfer Probability Threshold  
We deconstruct the structure of the DCFECF model, which only preserves the method of obtaining the transition probability threshold. We use the K-means clustering algorithm in the DCCF model to construct a global interaction graph without introducing time factors. The model with this structure is called DCFECF_1. The transition probability threshold in the DCCF model is still set at 0.5. The experimental results based on evaluation indicators Recall@10 and NDCG@10 on three datasets are shown in Tables 2 and 3. Figure 6: Comparative experiment results. From these tables, it can be seen that the performance of the DCFECF_1 model with the introduction of the transition probability threshold calculation method is always better than that of the DCCF model. This indicates that the transition probability threshold calculation method proposed in the DCFECF model can obtain different order transition probability thresholds based on data samples. Compared with the fixed transition probability threshold set in the DCCF model, it is more in line with practical application scenarios and can filter neighborhood information more effectively. (2) The Ablation Experiment of Clustering Algorithm  
We deconstruct the structure of the DCFECF model by not using the method of obtaining the transition probability threshold (which is a fixed value and still set to 0.5) and do not introduce a time factor. For clustering algorithms, we name the model that only uses the K-means++ clustering algorithm DCFECF_2. The model that only uses the fuzzy C-means clustering algorithm is DCFECF_3. The model using the fuzzy C-means clustering algorithm optimized based on the K-means++ clustering algorithm is DCFECF_4. The experimental results based on evaluation indicators Recall@10 and NDCG@10 on three datasets are shown in Tables 4 and 5. From these tables, it can be seen that because the K-means++ clustering algorithm is an improvement on the K-means clustering algorithm, the performance of DCFECF_2 is always better than DCCF. Compared to the K-means clustering algorithm, the fuzzy C-means clustering algorithm integrates the essence of fuzzy theory and can provide more flexible clustering results, so the performance of DCFECF_3 is always better than DCCF. DCFECF_4 \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Recall@10**} \\ \cline{2-4}  & **MovieLens-100K** & **Gowalla** & **Yelp2018** \\ \hline DCCF & 0.2326 & 0.1144 & 0.0315 \\ DCFECF\_2 & 0.2339 & 0.1156 & 0.0318 \\ DCFECF\_3 & 0.2341 & 0.1155 & 0.0319 \\ DCFECF\_4 & 0.2364 & 0.1171 & 0.0322 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Recall@10 performance under introducing different clustering algorithms. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Recall@10**} \\ \cline{2-4}  & **MovieLens-100K** & **Gowalla** & **Yelp2018** \\ \hline DCCF & 0.2328 & 0.1142 & 0.0315 \\ DCFECF\_1 & 0.2360 & 0.1168 & 0.0321 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Recall@10 performance under the method of calculating transfer probability threshold. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**NDCG@10**} \\ \cline{2-4}  & **MovieLens-100K** & **Gowalla** & **Yelp2018** \\ \hline DCCF & 0.6917 & 0.2938 & 0.1175 \\ DCFECF\_2 & 0.6951 & 0.2970 & 0.1189 \\ DCFECF\_3 & 0.6954 & 0.2968 & 0.1192 \\ DCFECF\_4 & 0.7001 & 0.3021 & 0.1204 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: NDCG@10 performance under introducing different clustering algorithms. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**NDCG@10**} \\ \cline{2-4}  & **MovieLens-100K** & **Gowalla** & **Yelp2018** \\ \hline DCCF & 0.6907 & 0.2939 & 0.1176 \\ DCFECF\_1 & 0.6997 & 0.3013 & 0.1202 \\ \hline \hline \end{tabular}
\end{table}