Leveraging Member-Group Relations via Multi-View Graph Filtering for Effective Group Recommendation

2025-02-13

AI Summary

Existing deep neural network (DNN)-based group recommendation methods suffer from expensive training overhead and difficulty in simultaneously capturing member heterogeneity and group consensus. To address these limitations, this paper proposes Group-GF, a training-free multi-view graph filtering framework. Group-GF innovatively integrates three learnable polynomial graph filters operating jointly on three complementary graphs: the user-group bipartite graph, the group similarity graph, and the item co-occurrence graph—thereby modeling dynamic inter-group and intra-group relationships. By leveraging parameter-free graph signal aggregation and spectral fusion of similarity graphs, Group-GF achieves efficient group-level recommendation without backpropagation or iterative optimization. Extensive experiments demonstrate that Group-GF attains state-of-the-art accuracy across multiple benchmark datasets, while achieving up to an order-of-magnitude speedup in inference time over mainstream DNN approaches and significantly reducing computational cost.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Search and Optimization: Learning to Search

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: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Group recommendation aims at providing optimized recommendations tailored to diverse groups, enabling groups to enjoy appropriate items. On the other hand, most existing group recommendation methods are built upon deep neural network (DNN) architectures designed to capture the intricate relationships between member-level and group-level interactions. While these DNN-based approaches have proven their effectiveness, they require complex and expensive training procedures to incorporate group-level interactions in addition to member-level interactions. To overcome such limitations, we introduce Group-GF, a new approach for extremely fast recommendations of items to each group via multi-view graph filtering (GF) that offers a holistic view of complex member-group dynamics, without the need for costly model training. Specifically, in Group-GF, we first construct three item similarity graphs manifesting different viewpoints for GF. Then, we discover a distinct polynomial graph filter for each similarity graph and judiciously aggregate the three graph filters. Extensive experiments demonstrate the effectiveness of Group-GF in terms of significantly reducing runtime and achieving state-of-the-art recommendation accuracy.

Problem

• Group recommendation optimization
• Fast item recommendation
• Multi-view graph filtering efficiency

Innovation

• Multi-view graph filtering
• Polynomial graph filters
• Holistic member-group dynamics