# Dual-Channel Feature Enhanced Collaborative Filtering Recommendation Algorithm
## 3 DCFECF Model
### Global Convolutional Channel  
The fuzzy C-means clustering algorithm has strong scalability and can handle large-scale data. Compared to the K-means clustering algorithm, this algorithm integrates the essence of fuzzy theory and can provide more flexible clustering results. However, the fuzzy C-means clustering algorithm randomly selects the initial clustering centers, and different clustering centers will affect the convergence of the membership function and still fall into local optima. The K-means++ clustering algorithm is improved on the basis of the K-means clustering algorithm to ensure the uniformity of the clustering centers. If the K-means++ clustering algorithm is used to determine the initial clustering center of the fuzzy C-means clustering algorithm, and then the fuzzy C-means clustering algorithm is used for clustering, it not only solves the problem of easily falling into local optima but also improves the clustering effect, thereby constructing a more comprehensive global interaction graph. In this section, we continue the setting of the global interaction graph and the information aggregation method within the global neighborhood in DCCF. In the global convolutional channel, a global interaction graph \(G_{global}\) is constructed using clustering based on the vector features of user nodes and item nodes. By using a fuzzy C-means clustering algorithm optimized based on K-means++ for clustering, if two nodes are filtered into the same category, a connecting edge is added between the two nodes to obtain a global interaction graph \(G_{global}\). In the local convolutional channel, the neighborhood feature information of each node is obtained. In order to reflect the structure of the nodes, the features of the nodes themselves are concatenated with the first-order neighborhood features:  
\[X_{C}=concat(X,LX) \tag{16}\]  
where \(L\) is the Laplacian matrix of adjacency matrix \(A\). Based on this, a node feature matrix \(X_{C}\) with first-order neighborhood information is obtained, and during the clustering process, user nodes and item nodes are treated as nodes of the same type for further classification. When using the fuzzy C-means clustering algorithm, the fuzzy index \(\rho\) is set to 2, and the iteration limit index \(eps\) is set to 0.0005. The process of constructing the global interaction graph \(G_{global}\) is listed in Algorithm 2:  
```
Input:\(X_{C}\) with first-order neighborhood feature information Output: Global interaction graph \(G_{global}\)
1. Randomly select one node from all nodes as the first clustering center, and use the K-means++ clustering algorithm to obtain \(k\) clustering centers. 2. Based on \(X_{C}\) with first-order neighborhood feature information, calculate the distance between each node and \(k\) cluster centers, calculate the membership degree according to Equation (3), obtain the initial membership matrix of the fuzzy C-means clustering algorithm, and calculate the objective function value according to Equation (1). 3. Obtain the final membership matrix using the fuzzy C-means clustering algorithm. 4. for each node in \(X_{C}\)do
5. Based on the feature vector \(X_{C}[node]\) of the current node node \(node\), the final membership matrix is used to obtain the membership degree of each cluster node belonging to each cluster center. 6. Sort according to the degree of membership, and classify the current node \(node\) into the category with the highest degree of membership. 7. Add all other nodes belonging to the category as neighboring nodes in the global neighborhood \(G_{global}[node]\) of current node \(node\). 8. endfor
9. return\(G_{global}\)
```  
**Algorithm 2**Construction of global interaction graph \(G_{global}\)  
The clustering results obtained from the algorithm process also require secondary filtering, that is, for the neighborhoods of all nodes, a fixed number of neighboring nodes with the closest feature performance are selected for each node within its category.