A Graph Convolution Network Based on Improved Density Clustering for Recommendation System

Abstract

Recommendation systems have been widely used in various applications to solve information overload and
improve user experience. Traditional recommendation algorithms mainly used Euclidean data for calculation
and abandoned the graph structure features in user and item data. Aiming at the problems in the current recommendation algorithms, this paper proposes an improved user density clustering method and extracts user
features through optimized graph neural network. Firstly, the improved density clustering method is used to
form the clustering subgraph of users based on the influence value of users. Secondly, the user data and item
data features of cluster subgraph are extracted by graph convolution network. Finally, the features of cluster
subgraphs are processed by global graph convolution network and the recommendation results are generated
according to the global graph features. This model not only improves the efficiency of decomposing large graph
into small graph through the improved user density clustering algorithm, but also extracts the features of user
groups through graph convolution neural network to improve the recommendation effect. The experiment also
proves the validity of this model.