Author Identifier (ORCID)
Jianxin Li: https://orcid.org/0000-0002-9059-330X
Abstract
With the increasing popularity of location-based social networks (LBSNs), recommendation based on LBSNs has attracted wide attention in academic and industrial domains. Traditional recommendation systems face critical challenges in handling data sparsity and underutilized social trust. Existing traditional models particularly struggle to incorporate both trust-level differences and implicit relationships effectively within large-scale personalized recommendations. This limitation directly leads to aggravated data sparsity, further exacerbating cold-start scenarios and significantly reducing recommendation accuracy for long-tail items. Existing widely-adopted deep learning models, such as graph convolutional networks (GCNs), apply equal propagation weights to all neighbor nodes, which not only causes feature convergence between high-trust and ordinary users (over-smoothing) but also incurs substantial computational overhead. We propose the Trust-Based Core Graph Collaborative Filtering (TCGCF) framework, integrating trust-weighted core graph analysis with trust-constrained graph convolution. TCGCF captures both explicit and implicit trust relationships, enhances personalization, and mitigates over-smoothing. Our trust-weighted core graph analysis identifies influential users in information propagation, while the trust-constrained convolution scheme enables precise, differentiated information flow. Experiments on real-world datasets demonstrate that TCGCF improves recommendation accuracy and computational efficiency, outperforming existing models in precision, recall, and suitability for large-scale applications.
Document Type
Journal Article
Date of Publication
8-1-2025
Volume
285
Publication Title
Expert Systems with Applications
Publisher
Elsevier
School
School of Business and Law
Funders
National Natural Science Foundation of China (62076224)
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Comments
This is an Authors Accepted Manuscript version of an article published by Elsevier in Expert Systems With Applications. The published version is available at: https://doi.org/10.1016/j.eswa.2025.127899
Xie, T., Chen, Y., Wu, Y., Cui, N., Chen, H., Lu, X., Huang, X., Wang, Y., & Li, J. (2025). Trust-based core social graph convolution: An innovative framework for location recommendation. Expert Systems With Applications, 285, 127899. https://doi.org/10.1016/j.eswa.2025.127899