# Dual-Channel Feature Enhanced Collaborative Filtering Recommendation Algorithm
## 5 Conclusions  
This paper proposes a dual-channel feature enhanced collaborative filtering recommendation algorithm (DCFECF) to solve the problem that in the DCCF model, the fixed threshold of transition probability leads to a decrease in the filtering effect of neighborhood information, the K-means clustering algorithm tends to make the clustering results fall into local optimization, resulting in incomplete global interaction graph, and does not consider the impact of time factors on the prediction results. In the local convolutional channel, transforming the transition probability threshold from a fixed value to a dynamic value can better filter neighborhood information and improve the predictive ability of the  
\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 & 0.2325 & 0.1141 & 0.0314 \\ DCFECF1 & 0.2338 & 0.1146 & 0.0316 \\ DCFECF2 & 0.2344 & 0.1155 & 0.0320 \\ DCFECF3 & 0.2366 & 0.1173 & 0.0324 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Recall@10 performance under introducing the time factor.  
\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 & 0.6910 & 0.2936 & 0.1173 \\ DCFECF1 & 0.6931 & 0.2949 & 0.1178 \\ DCFECF2 & 0.6948 & 0.2971 & 0.1189 \\ DCFECF3 & 0.7004 & 0.3019 & 0.1206 \\ \hline \hline \end{tabular}
\end{table}
Table 7: NDCG@10 performance under introducing the time factor.  
model; in the global convolutional channel, the K-means++ clustering algorithm is first used to determine the clustering center, and then the fuzzy C-means clustering algorithm is used to solve the local optimal problem, which can construct a more comprehensive global interaction graph, simultaneously introducing the time factor to further modify the predicted results; through experiments, the rationality and effectiveness of the DCFECF model in improving the DCCF model were verified.  
In future research, we will focus on two aspects, namely the expression of interaction relationships between nodes and the construction of interaction graphs to modify the DCFECF model. We will modify the attention mechanism [19], adaptively learn and fuse weights, and further express the interaction relationships between nodes after determining their neighborhood range, thereby improving recommendation performance. In addition, we will design different custom rules to construct interaction diagrams [20], combine different information, and construct more comprehensive global interaction diagrams, making the model more interpretable.  
[PERSON] designed the proposed method, performed the experiments, analyzed the data, and wrote the paper. B.N. modified the paper and offered support. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 62072326.  
Not applicable.  
The authors declare no conflict of interest.