GLARA: A Global–Local Attention Framework for Semantic Relation Abstraction and Dynamic Preference Modeling in Knowledge-Aware Recommendation
Year: 2025
Type: article
Abstract: Knowledge graph-enhanced recommendation has gained increasing attention for its ability to provide structured semantic context. However, most existing approaches struggle with two critical challenges: the sparsity of long-tail relations in knowledge graphs and the lack of adaptability to users’ dynamic preferences. In this paper, we propose GLARA, a novel recommendation framework that combines semantic abstraction and behavioral adaptation through a two-stage modeling process. First, a Virtual Relational Knowledge Graph (VRKG) is constructed by clustering semantically similar relations into higher-level virtual groups, which alleviates relation sparsity and enhances generalization. Then, a global Local Weighted Smoothing (LWS) module and a local Graph Attention Network (GAT) are integrated to jointly refine item and user representations: LWS propagates information within each virtual relation subgraph to improve semantic consistency, while GAT dynamically adjusts neighbor importance based on recent interaction signals. Extensive experiments on Last.FM and MovieLens-1M demonstrate that GLARA outperforms state-of-the-art methods, achieving up to 5.8% improvements in NDCG@20, especially in long-tail and cold-start scenarios. Additionally, case studies confirm the model’s interpretability by tracing recommendation paths through clustered semantic relations. This work offers a flexible and interpretable solution for robust recommendation under sparse and dynamic conditions.
Source: Applied Sciences
DOI: 10.3390/app15126386
Authors: Runbo Liu, Lili He, Junhong Zheng
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Award: G782244865
OpenAlex ID: W4411096273