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App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 1

Session-based recommendation enhancement framework based on large language models and graph neural network

Yu Enhai
Wen Yan
Chen Yu'ao
College of Computer Science & Engineering, Shandong University of Science & Technology, Qingdao Shandong 266590, China
DOI: 10.19734/j.issn.1001-3695.2025.06.0201

Abstract

With the widespread application of session-based recommendation (SBR) , the authors addressed the critical challenges of utilizing semantic information, modeling cross-session user interests, and suppressing data noise to improve recommendation performance. They developed LGSBR, a novel framework that integrates large language models (LLMs) and graph neural networks (GNNs) to achieve semantic enhancement and personalized recommendation. Specifically, they generated supplementary text embeddings for items and cross-session user interest embeddings using LLMs and fine-tuned language models, fused these with ID embeddings through a soft attention mechanism to create semantically rich representations, incorporated user interest embeddings with alignment loss for personalized recommendations, and applied two-stage weight learning to filter noisy items and optimize session representations. Experiments demonstrated that LGSBR achieved P@20 of 21.38% and MRR@20 of 6.76% on the Beauty dataset, improving over the SR-GNN baseline by 23.3% and 50.56%, respectively, and P@20 of 25.86% and MRR@20 of 7.58% on the ML-1M dataset, with gains of 12.63% and 10.98%. The study confirms LGSBR’s generality and effectiveness across multiple GNN models.

Foundation Support

国家自然科学基金资助项目(52374221)
国家重点研发计划资助项目(2022ZD0119501)
山东省自然科学基金资助项目(ZR2022MF288,ZR2023MF097)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.06.0201
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 1

Publish History

[2025-09-17] Accepted Paper

Cite This Article

于恩海, 温彦, 陈宇翱. 基于大语言模型与图神经网络的会话推荐增强框架 [J]. 计算机应用研究, 2026, 43 (1). (2025-09-17). https://doi.org/10.19734/j.issn.1001-3695.2025.06.0201. (Yu Enhai, Wen Yan, Chen Yu'ao. Session-based recommendation enhancement framework based on large language models and graph neural network [J]. Application Research of Computers, 2026, 43 (1). (2025-09-17). https://doi.org/10.19734/j.issn.1001-3695.2025.06.0201. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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