# Light Graph Convolutional Recommendation Algorithm Based on Hybrid Spreading
## 2 Related Work
### Recommendation Algorithm Based on a Graph Convolutional Network  
Graph Convolutional Networks (GCNs), a powerful graph-structured data processing method, have been widely adopted in recommendation systems. By incorporating neighborhood information, GCNs enable the propagation and updating of node features through the graph structure, effectively capturing relationships between nodes [12]. In recommendation systems, GCN can model user-item interaction data and capture user interests, thereby improving the accuracy and diversity of recommendations. For instance, NGCF leverages GCNs to learn the latent embedding representations between nodes through graph convolutional networks, extracting higher-order information from user-item interaction data. This demonstrates that graph convolutional embedding propagation improves the quality of embedding for both users and items [13]. To address the limitations of GCN in terms of complexity and computational efficiency, LightGCN simplifies the hierarchical structure of GCNs by removing redundant nonlinear transformations and feature projections, focusing directly using neighborhood information for node embedding updates. This significantlyreduces computational complexity [14]. [PERSON] et al. optimized the loss function of LightGCN based on contrastive learning and proposed a novel filtering loss function that automatically filters out low-information negative samples [15]. [PERSON] et al. utilized a multi-layer feature fusion strategy in LightGCN to capture the complete feature embedding of nodes, improving recommendation performance [16]. [PERSON] et al. used the adjacency matrix to optimize the embedding of users and items, and introduced layer attenuation coefficients during convolutional layer aggregation to distinguish the importance of different layers. This allows the model to fully utilize historical user-item interaction data for better learning of user preferences [17]. [PERSON] et al. utilized a mapping matrix at the embedding layer to transform initial feature vectors into adaptive embeddings and introduced an attention mechanism to aggregate different aspects of user embeddings, thereby learning latent embedding representations of users and items to enhance recommendation effectiveness [18]. [PERSON] et al. combined user social behavior data, user-item interaction data, and item relationships to explicitly construct a connection between items, enabling better learning of user preferences [19]. [PERSON] proposed a cross-design network user identification model based on bidirectional GCN, which utilizes the Jaccard similarity coefficient to calculate user intimacy and constructs an adjacency matrix to represent user relationships in social networks, subsequently extracting user information across social networks [20]. [PERSON] et al. constructed a feature matrix reflecting user preferences based on ratings, and utilized a breadth-first search to transform the bipartite network into a compact low-dimensional vector. This approach integrates rating behavior into the graph convolutional network while preserving the topological structure and interconnectivity [21].  
These works have improved the accuracy of recommendations and demonstrated the effectiveness of using the GCN framework to mine latent embedding representations of users and items in recommendation systems.