Rethinking information fusion: Achieving adaptive information throughput and interaction pattern in graph convolutional networks for collaborative filtering

Abstract

Graph convolutional networks (GCNs) are popular in collaborative filtering because they have robust information fusion mechanisms. However, existing GCN-based models generally treat all nodes uniformly within a bipartite graph, such as the classic e-commerce scenario with user nodes and item nodes, which overlooks variations in node types and node information amount. Furthermore, these models rarely consider the adaptability of different information fusion methods to various user interaction patterns across different scenarios. These oversights may result in the loss of node information and inadequate representation learning, thereby potentially leading to sub-optimal model performance and efficiency. To address these problems, we propose an adaptive GCN-based model, called AdaptGCN, which handles both node and layer heterogeneity. AdaptGCN introduces a new node information fusion method to achieve adaptive information throughput according to node type and node information amount. It also has different layer information fusion methods to handle various interaction patterns across scenarios. For the node information amount, we provide the corresponding definition and explore properties for quantifying such information. Based on the quantification properties, we design a unified quantification function and develop it into an adaptive quantification method according to the characteristics of different types of nodes, which we use for node information fusion in AdaptGCN to achieve adaptive information throughput for each node. Based on this, we further propose a unified layer information fusion method to address the varying interaction patterns across different scenarios. Extensive experimental results show that AdaptGCN outperforms state-of-the-art GCN-based models in terms of both performance and efficiency.