A collaborative filtering model based on heterogeneous graph neural network

[Objective] Collaborative filtering algorithms are widely used in various recommendation systems and can be used to recommend information of interest to users similar to the user based on historical data. Recently, collaborative filtering algorithms based on graph neural networks have become one of the hot research topics. A collaborative filtering model based on a graph structure usually encodes the interaction between users and information items as a two-part diagram, and high-order connectivity modeling of the bipartite graph can be used to capture the hidden relationship between the user and the item. However, this bipartite graph model does not explicitly obtain the similarity relationship between users and between items. Additionally, the bipartite graph sparsity causes high-order connectivity dependence problems in the model.[Methods] Herein, a collaborative filtering model is proposed based on a heterogeneous graph convolutional neural network that explicitly encodes the similarities between users and that between items into the graph structure so that the interaction relationships between users and between items are modeled as a heterogeneous graph. The heterogeneous graph structure allows the similarities between users and between items to be directly captured, reducing the need for high-order connectivity and alleviating the bipartite graph sparsity problem.[Results] We conducted experiments on four typical datasets and compared the results using four typical methods. The results showed that our model achieved better experimental results than the traditional collaborative filtering models and existing graph neural network models. Moreover, based on the different types of edges, different similarity methods, and different similarity thresholds, our model obtained better experimental results.[Conclusions] Our model explicitly encodes the similarities between users and between items into the heterogeneous graph structure as edges so that the model can directly learn these similarities during training to get the embedded information of users and items. The proposed model alleviates the sparsity and high-order connectivity modeling problems of bipartite graphs. The collaborative filtering model based on heterogeneous graph neural networks can also fully capture the interaction relationships between users and items through low-order connectivity in the graph. © 2023 Press of Tsinghua University. All rights reserved.