Work

Prune and Distill: A Novel Knowledge Distillation Method for GCNs-Based Recommender Systems

Year: 2025

Type: article

Abstract: Graph convolutional networks (GCNs)-based recommenders have demonstrated remarkable recommendation performances but suffer from prohibitive computational cost, limiting their practical deployment. Knowledge distillation provides an effective approach to address this challenge by training a compact student model to inherit knowledge from a complex teacher, balancing accuracy and efficiency. However, existing knowledge distillation methods primarily focus on optimizing loss functions while neglecting the critical role of the graph structure for the student model in capturing personalized preferences. In this paper, we propose Prune and Distill (PAD), a novel knowledge distillation framework that refines the graph structure of the student by pruning redundant connections and enriching informative adjacencies under the guidance of the teacher model. By directly enhancing the structural representation of the student model, PAD facilitates effective preference while preserving competitive recommendation performance. Extensive experiments on three benchmark datasets demonstrate that PAD-enhanced student recommender outperforms state-of-the-art recommendation models with significantly reduced computational overhead, validating PAD’s superiority in effectiveness and efficiency for real-world applications.

Source: IEEE Access

DOI: 10.1109/access.2025.3573181

Authors: Peng Yi, Zhaoxian Li, Lu Chen, Cheng Yang, Xiongcai Cai

Institutions: State Key Laboratory of Media Convergence and Communication, Communication University of China, University of Copenhagen, UNSW Sydney

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,075

Funder: Communication University of China