Lbgcn: Lightweight bilinear graph convolutional network with attention mechanism for recommendation

Yu Su, Pingzhu Wei, Linbo Zhu, Lixiang Xu, Xianquan Wang, He Tong, Ze Han

Abstract: The Graph Convolutional Neural Network (GCN) is a powerful technique for learning and representing graph data, commonly utilized in model-based collaborative filtering recommendation algorithms. However, despite its effectiveness, the issues are data sparsity and interpretability. Most existing GCN-based models simply update the central node’s features by aggregating the features of its neighbors, typically via a weighted sum. Unfortunately, this approach fails to capture the cooperative information hidden in the neighbor interactions. To address this limitation, we propose a recommendation algorithm based on a convolution network of lightweight neighborhood interactive graphs, named the Lightweight Bilinear Graph Convolutional Network (LBGCN). Our approach employs a lightweight graph convolutional neural network as a multi-level feature aggregator, leveraging higher-order connectivity to aggregate neighborhood information into a multi-level feature of the node through the aggregator. Meanwhile, we introduce a local feature aggregator to capture the collaborative filtering signals in the interaction features of neighbors. Finally, we combine the results using an attention mechanism to obtain the embedded representation of final users and items. In addition, we demonstrate the rationality and effectiveness of our proposed model through experiments on three public datasets. The results show that our method could gain 2.52% NDCG improvement at most.

External IDs:dblp:journals/apin/SuWZXWTH25