# Graph Representation-Based Deep Multi-View Semantic Similarity Learning Model for Recommendation
## 1 Introduction  
With the rapid development of Internet technology, mining and analyzing massive network information has become a hot topic and a challenging problem. A recommendation system can help users solve the problem of information overload when there is no clear demand or a huge amount of information, and provide users with accurate and fast business information (such as about goods, projects, services, etc. ), which has became a common interest and research hotspot of industry and academia in recent years [1]. However, there are various types of data and wide application scenarios. Recommendation systems will encounter the challenges of cold start and sparse matrix. The collaborative filtering technique is the most widely used method in recommendation systems, which predicts future preferences of users by analyzing their historical behavioral data [2; 3; 4]. At present, collaborative filtering has been favored by many researchers in the field of recommendation. The fusion of collaborative filtering technology with the recommendation method can effectively solve the problems of cold start and sparse matrix in traditional recommendation systems, and improve the performance and accuracy of recommendation systems. However, data sparsity and cold start are the main problems that limit the performance of collaborative filtering recommendation algorithms. The emergence of social networks has led to an increasing number of recommendation algorithms, such as SoC, RSTE, SocialMF, TrustMF, and so forth. These use the rich information provided by social networks to address the recommendation problem. However, in primitive social networks, trust relationships tend to be binary. Intuitively, this trust relationship representation is coarse-grained due to the differences in the intensity of users' trust in different people. In addition, the sparse trust relationships in social networks make fewer social relationship data available [5]. In fact, many users do not have direct trust relationships. However, they have many common neighbors who are likely to have high levels of trust between them. Considering such indirect, implicit trust relationships when modeling recommendation models can effectively improve the performance of recommendation models. However, traditional social network-based recommendation algorithms tend to ignore these implicit trust relationships. To model the global and local structure of information networks, a series of graph embedding (GE) models were proposed. Graph embedding models embed large-scale information networks into a low-dimensional space, where a low-dimensional feature vector represents each network node. This low-dimensional feature vector effectively preserves the structural information of the information network and can be applied in various graph-based machine learning tasks, such as node classification, clustering, link prediction and visualization. The GraphSAGE model is an inductive learning framework that can efficiently generate unknown vertices embedding by using the attribute information of vertices [6; 7]. It is used to derive the user trust relationships from the original social network that hold both local and global information of the social network [8; 9; 10], and a graph-embedded model-based collaborative filtering recommendation algorithm is proposed. Intuitively, the low-dimensional feature representation of user nodes in social networks can be learned through graph embedding and can be integrated into traditional social network-based recommendation algorithms to address the problems of coarse granularity. However, few research efforts have considered integrating graph embedding techniques into traditional social network-based recommendation algorithms to improve the performance of traditional social network-based recommendation. **The Method. ** This paper proposes a collaborative filtering recommendation algorithm based on a graph embedding model to address the problems of traditional social network-based recommendation algorithms.