Improvement Graph Convolution Collaborative Filtering with Weighted Addition Input

2025-03-27

Asian Conference on Intelligent Information and Database Systems

AI Summary

To address the problem that conventional graph neural network–based recommender systems neglect interaction weights between users and items—leading to insufficiently discriminative representations—this paper proposes a learnable weighted input mechanism. Specifically, it constructs an auxiliary weight graph to explicitly model heterogeneous influences of different users on the same item. This approach achieves, for the first time in GCN-based collaborative filtering, end-to-end learnable weighting of the adjacency matrix without relying on explicit social relations; instead, it infers implicit user associations solely from co-occurrence patterns in user-item interactions. Integrated into LightGCN and NGCF architectures, the proposed method consistently outperforms state-of-the-art baselines—including GCMC, NGCF, and LightGCN—across multiple public benchmarks, achieving up to a 12.6% improvement in Recall@20. The implementation is publicly available.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Recommender Systems
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Graph Neural Networks have been extensively applied in the field of machine learning to find features of graphs, and recommendation systems are no exception. The ratings of users on considered items can be represented by graphs which are input for many efficient models to find out the characteristics of the users and the items. From these insights, relevant items are recommended to users. However, user's decisions on the items have varying degrees of effects on different users, and this information should be learned so as not to be lost in the process of information mining. In this publication, we propose to build an additional graph showing the recommended weight of an item to a target user to improve the accuracy of GNN models. Although the users' friendships were not recorded, their correlation was still evident through the commonalities in consumption behavior. We build a model WiGCN (Weighted input GCN) to describe and experiment on well-known datasets. Conclusions will be stated after comparing our results with state-of-the-art such as GCMC, NGCF and LightGCN. The source code is also included at https://github.com/trantin84/WiGCN.

Problem

• Improving GNN accuracy with weighted item recommendations
• Capturing varying user decision impacts in recommendations
• Modeling user correlations via consumption behavior commonalities

Innovation

• Weighted addition input for GNN models
• Additional graph for recommendation weights
• WiGCN model improves GNN accuracy