Identification of perceptive users based on the graph convolutional network

In this paper, we introduce a pioneering approach titled Identifying Perceptive Users based on Graph Convolutional Networks (IPGCN), tailored specifically for user-object bipartite networks. Our methodology incorporates rating behavior into the graph convolutional network framework, crafting a feature matrix that encapsulates the intricacies of user preferences. This feature matrix is meticulously constructed using a breadth-first search algorithm, grounded in user rating attributes, effectively transforming the bipartite network into a compact, low-dimensional vector space. This transformation not only serves as the input for our graph convolutional network but also meticulously preserves the topological structure and interconnectivity of the nodes. Furthermore, we introduce a novel training paradigm by leveraging the classification outcomes from a perceptive user quantification model. This model discriminates between perceptive users, defined as the top q% (with 0 <q < 1) most insightful users based on their ratings, and non-perceptive users. This binary classification serves as the ground truth for training our IPGCN model. Through rigorous experimentation on three real-world datasets, we demonstrate that our IPGCN method outperforms traditional machine learning algorithms like SVM, XGBoost, and Random Forest, as well as state-of-the-art graph neural network approaches including GCN and GAT. Specifically, when q = 5%, our method achieves remarkable improvements of 25.28% in Accuracy, 23.34% in F1 Score, and 25.30% in AUC, highlighting the efficacy and superiority of our proposed IPGCN approach in identifying perceptive users within complex bipartite networks.