# A Multi-Behavior Recommendation Method for Users Based on Graph Neural Networks
## 2 Related Work  
Most previous recommendation models [13; 14; 15; 16] have been designed for a single type of behavior, and in most cases, behaviors directly related to platform profits were selected for modeling, such as purchase behavior in e-commerce platforms. In practice, however, user behavior is inherently multi-typical (e.g., browsing, favoriting, purchasing, etc.). Different types of user behaviors may exhibit different semantic information to characterize the diverse user-goods interactions. The existing user-commodity interactions are thus coding functions and are not sufficient to comprehensively learn complex user preferences. Moreover, using only a single behavior may lead to severe cold-start or data sparsity problems. For example, on an e-commerce website, it is difficult to construct a recommendation model based on purchase behavior alone to provide a comprehensive learning model for users without historical purchases, and new users with a purchase history can be aptly recommended.  
While realizing the importance of leveraging different types of user behavior at the same time, encoding multiple types of behavioral patterns poses a significant challenge. These different types of interaction behaviors may interrelate in complex ways, providing complementary information for learning about user interests. In addition, although several multi-behavioral user modeling techniques have emerged in recent years, some multi-behavioral user modeling techniques [8; 11] have emerged for recommendation, but they fail to capture higher-order information in different user-goods relationships. Inspired by this, applying graph neural networks to recommendations [17; 18] is beneficial to consider user-goods interactions in the embedding space higher-order relationships between user-goods interactions are considered in the embedding space.  
Recently, graph neural networks have achieved promising results in learning dependencies from graph-structured data [17]. Typically, the core of graph neural networks is to aggregate feature information of neighboring nodes on the graph under a message propagation mechanism [18]. This information dissemination mechanism aggregates the information of higher-order neighbors through nodes, which can further capture higher-order interrelationships and achieve representation learning effectively. In other words, graph neural networks can better solve relationship inference problems as an interpretable model. The most representative of these was the Graph Convolutional Network (GCN), which obtains the representation of the current node by combining the weighted values of neighboring nodes' egress and ingress. Inspired by the effectiveness of graph convolutional networks, recent studies, such as PTGCN [19] and GraphSage [20], utilize graph convolutional networks to explore the user-item interaction graph and aggregate the embeddings of neighboring nodes. These works propagate information among nodes to mine relationships between users and items. Then, graph convolutional networks became a popular research direction, and researchers have conducted a lot of work to study heterogeneous graphs. BiHGH [21] is a new bidirectional heterogeneous graph hashing method. First, it uses heterogeneous graph nodes to initialize then design an Ambigram convolution algorithm to sequentially transfer information, and finally uses Bayesian personalized sorting loss combined with dual similarity preserving regularization to achieve user preference learning. PFCM [22] created a heterogeneous graph that unifies users, items, and attributes and designed a user embedding module based on multimodal content representation to learn user representations. Finally, heterogeneous graph learning was implemented by executing meta path guidance.