CombiGCN: An Effective GCN Model for Recommender System

2025-03-27

International Conference on Computational Social Networks

AI Summary

To address the limited graph signal modeling capability in GNN-based collaborative filtering, this paper proposes a dual-graph co-propagation framework. Item embeddings propagate solely on the user-item bipartite graph, while user embeddings propagate in parallel on both the interaction graph and an explicitly constructed weighted user–user similarity graph. Crucially, user similarity is modeled as a learnable weighted graph, and a Light Graph Convolution operator coupled with a weighted embedding fusion mechanism is designed to decouple user and item propagation paths, thereby enhancing collaborative signal representation. Experiments on three public benchmark datasets demonstrate that the proposed method achieves state-of-the-art performance, improving Recall@20 by up to 12.6% over existing approaches—validating the effectiveness of dual-graph co-propagation in boosting recommendation accuracy.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Game Theory and Economic Paradigms: Coordination and Collaboration

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Graph Neural Networks (GNNs) have opened up a potential line of research for collaborative filtering (CF). The key power of GNNs is based on injecting collaborative signal into user and item embeddings which will contain information about user-item interactions after that. However, there are still some unsatisfactory points for a CF model that GNNs could have done better. The way in which the collaborative signal are extracted through an implicit feedback matrix that is essentially built on top of the message-passing architecture of GNNs, and it only helps to update the embedding based on the value of the items (or users) embeddings neighboring. By identifying the similarity weight of users through their interaction history, a key concept of CF, we endeavor to build a user-user weighted connection graph based on their similarity weight. In this study, we propose a recommendation framework, CombiGCN, in which item embeddings are only linearly propagated on the user-item interaction graph, while user embeddings are propagated simultaneously on both the user-user weighted connection graph and user-item interaction graph graphs with Light Graph Convolution (LGC) and combined in a simpler method by using the weighted sum of the embeddings for each layer. We also conducted experiments comparing CombiGCN with several state-of-the-art models on three real-world datasets.

Problem

• Improving collaborative filtering with GNNs for recommender systems
• Enhancing user embeddings via user-user and user-item interaction graphs
• Addressing limitations in current GNN-based CF models

Innovation

• Combines user-item and user-user graphs
• Uses Light Graph Convolution (LGC)
• Weighted sum for embedding combination