# A Multi-Behavior Recommendation Method for Users Based on Graph Neural Networks
## 1 Introduction  
Personalized recommendations function to provide users with appropriate products according to user preferences. Determining how to accurately capture user preferences from user behaviors is the core issue of personalized recommendations. Traditional recommendation models [1] usually only rely on a single behavior for a recommendation, which makes them insufficient when extracting complex cooperative signals from users' multi-type behaviors [2]. Meanwhile, there are serious data sparsity [3, 4] and cold-start problems [5, 6], especially for certain high-cost and low-frequency behaviors. In the real world, users usually have different types of interactive behaviors. In the face of diversified user behaviors, a big challenge to achieve more accurate recommendations is whether users' heterostructure behavior data can be processed more finely. The multi-behavior recommendation model jointly considers different types of behavioral semantics, which is of great help to predict the possibility of users adopting target behaviors [7]. For example, on an e-commerce platform, users' page browsing, shopping-cart additions, and collection behaviors for different items can be used as auxiliary information to help predict users' purchase intent (target behavior) tasks. Therefore, considering the complex dependencies between multiple behaviors is crucial to accurately predict user preferences. In order to make full use of dynamic interaction information to better predict user preferences, several multi-behavioral recommendation models have emerged in recent years [2, 8]. LightGCN [9] learns user/commodity embeddings via an interaction graph by propagating linearly over the interaction graph, using the weighted sum of the embeddingslearned at each layer as the final embedding. This simple, linear, and neat model is easier to implement and train but does not take into account the variability between behaviors. To distinguish the semantics of different behavior types, the KHGT [10] model assigns different learnable weights to different edges in the user-goods heterogeneous graph and clearly distinguishes which type of user-goods interaction is more important to assist in the task of predicting the target behavior. Nowadays, recommendations based on a graph neural network have been used in many real-world scenarios. The NetEase Cloud Music App introduces the graph model architecture, takes a variety of different types of songs as nodes, and constructs a graph relationship network through the multi-type behavior relationship between users and songs. Jingdong Mall also adopts the model based on a graph neural network proposed by the Jingdong platform, and more accurate recommendation results bring huge benefits to the platform. Despite the success of these approaches in multi-behavior recommendation tasks, there are some limitations:  
1. Different types of behaviors can characterize user preferences from different dimensions and complement each other for better learning of user preferences. User/commodity embedding is at the core of recommendation systems. Most current user/commodity embedding representations are a fusion of static features and lack the explicit encoding of a synergistic signal, which is hidden in the user-commodity interaction. Therefore, it is challenging and valuable to capture the behavioral diversity and potential dependencies in recommendations. To address this challenge, existing work models behavioral dependencies by generating specific types of behavioral embeddings through different aggregation approaches to enhance the user/goods representation. For example, MATH [11] uses self-attentiveness to encode pairwise correlations between different types of behaviors and make predictions about the target behavior. 2. Traditional multi-behavior recommendation models are implemented based on sequential models, which tend to focus more on the local perspective of multiple sequential behaviors of users.