# Dual-Channel Feature Enhanced Collaborative Filtering Recommendation Algorithm
## 1 Introduction  
The rapid development of the internet has led to the rapid growth of different types of data, resulting in the problem of \"information overload\". It is not easy for users to efficiently select the content they are interested in when facing massive amounts of network data. The recommendation model [1] is a reliable and effective technique that can recommend content of interest to users based on their historical behavior records, thereby alleviating the problem of \"information overload\". Collaborative filtering is widely used in recommendation models, which is a recommendation algorithm based on user behavior data. In recent years, driven by the demand for relationship modeling in recommendation task scenarios, building collaborative filtering recommendation models [2; 3] using graph convolution networks has become mainstream.  
However, the collaborative filtering model based on graph convolution networks has the following problems: Firstly, graph convolution networks have the over-smoothing problem [4; 5]. The node features transferred to the deep layer gradually become similar, and the preference features in the recommendation process become homogeneous, which reduces the model performance. Secondly, the adjacency matrix is used to express the interaction relationship between users and items, focusing on the feature aggregation [6] of the local neighborhood, and it is impossible to mine the interaction pattern between nodes through the overall structure of the graph.  
The existing solutions represented by the dual-channel graph collaborative filtering recommendation algorithm [7] (DCCF), on the one hand, introduce the transition matrix, setthe transition probability threshold, determine the neighborhood range of nodes, and adopt a single-layer network structure to suppress the over-smoothing problem in the graph convolution network iteration process; on the other hand, clustering is used to construct a global interaction graph, model the potential interaction between nodes, supplement global information, and overcome the problem that the interaction information is limited to the local neighborhood.  
The above solution has the following problems: firstly, the transition probability threshold is set to a fixed value and does not dynamically change the transition probability threshold based on different samples, which will reduce the accuracy of the prediction results; secondly, the K-means clustering algorithm used by DCCF for clustering is sensitive to the initial clustering center, which makes it easy for the clustering result to fall into local optimum and is not conducive to the construction of a comprehensive global interaction graph; finally, users' interests will change over time and DCCF does not consider the impact of time factors on user preferences.  
To solve the above problems, this paper proposes a dual-channel feature enhanced collaborative filtering recommendation algorithm (DCFECF). The contributions of this paper are summarized as follows:  
1. In the local convolutional channel, we introduce a self-attention mechanism to calculate the weight value of each edge and use the weighted average method to calculate the threshold of neighborhood transition probability for each order. The transfer probability threshold is converted from a fixed value to a dynamic value.
2. In the global convolutional channel, we use the K-means++ clustering algorithm to determine the clustering center and then use the fuzzy C-means clustering algorithm to replace the K-means clustering algorithm for clustering, solving the local optimal problem and enabling the construction of a more comprehensive global interaction graph.
3. At the same time, we introduce a time factor into the model and modify the existing time factor expression to further improve the predicted results.