Abstract

With the rapid development of Internet technology, how to mine and analyze massive amounts of network information to provide users with accurate and fast recommendation information has become a hot and difficult topic of joint research in industry and academia in recent years. One of the most widely used social network recommendation methods is collaborative filtering. However, traditional social network-based collaborative filtering algorithms will encounter problems such as low recommendation performance and cold start due to high data sparsity and uneven distribution. In addition, these collaborative filtering algorithms do not effectively consider the implicit trust relationship between users. To this end, this paper proposes a collaborative filtering recommendation algorithm based on graphsage (GraphSAGE-CF). The algorithm first uses graphsage to learn low-dimensional feature representations of the global and local structures of user nodes in social networks and then calculates the implicit trust relationship between users through the feature representations learned by graphsage. Finally, the comprehensive evaluation shows the scores of users and implicit users on related items and predicts the scores of users on target items. Experimental results on four open standard datasets show that our proposed graphsage-cf algorithm is superior to existing algorithms in RMSE and MAE.

Highlights

  • With the rapid development of Internet technology, mining and analyzing massive network information has become a hot topic and a challenging problem

  • This paper proposes a collaborative filtering recommendation algorithm based on a graph embedding model to address the problems of traditional social networkbased recommendation algorithms

  • In order to verify the performance of the GraphSAGE collaborative filtering recommendation algorithm, a comparison analysis with other popular recommendation algorithms is performed on a real dataset

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Summary

Introduction

With the rapid development of Internet technology, mining and analyzing massive network information has become a hot topic and a challenging problem. 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]. 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. Data sparsity and cold start are the main problems that limit the performance of collaborative filtering recommendation algorithms

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