App. Res. of Comp. Accepted Paper
Vol. 44, 2027 No. 2

Lmgr：enhanced multi-view graph collaborative recommendation model integrated with large language models

Zhang Shengshai2,3
Shang Mengyang1
Yu Xiaodong3
Mei Shaowei1
1. School of Computer and Information Engineering, Shanghai Second Polytechnic University, Shanghai 201209, China
2. Business School, University of Shanghai Science and Technology, Shanghai 201209, China
3. School of Information Science and Technology, Sanda University, Shanghai 201209, China
DOI: 10.19734/j.issn.1001-3695.2026.06.0157

Abstract

To address the weak cold-start problem of long-tail courses with sparse interactions on MOOC platforms, and the "pseudo-semantic" noise caused by concatenating random IDs in existing cross-modal approaches, a highly efficient denoising cross-modal collaborative recommendation model was constructed. Specifically, a Large Language Model-enhanced Multi-view Graph Recommendation (LMGR) architecture was proposed. Abandoning traditional ID embeddings, LMGR utilized pre-trained BERT to fuse text and metadata at the input level. It generated initial pure-semantic node representations by aggregating interaction edge features to cut off pollution at the source. Concurrently, three parallel graph convolutional branches—explicit intensity, implicit connectivity, and item domain—were constructed to decouple enrollment motivations. A multi-head self-attention engine further performed cross-modal sequence denoising and purification. Extensive experiments on real-world MOOC datasets show that LMGR achieves NDCG@5 improvements of 63.20% and 79.14% over the strongest baselines, respectively. By combining pure-semantic initialization with multi-dimensional topological compensation, LMGR successfully blocks underlying semantic pollution and achieves deep cross-modal alignment, effectively mitigating the weak cold-start issue and significantly improving recommendation accuracy in vertical scenarios.

Foundation Support

国家自然科学基金资助项目(12172228)
上海市教育科研资助项目(C2026297,C2025299)
上海杉达学院校级基金资助项目(2024YB09)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.06.0157
Publish at: Application Research of Computers Accepted Paper, Vol. 44, 2027 No. 2

Publish History

[2026-10-21] Accepted Paper

Cite This Article

张圣筛, 尚梦杨, 于晓东, 等. ULMGR：融合大语言模型的增强多视图图协同推荐模型 [J]. 计算机应用研究, 2027, 44 (2). (2026-10-24). https://doi.org/10.19734/j.issn.1001-3695.2026.06.0157. (Zhang Shengshai, Shang Mengyang, Yu Xiaodong, et al. Lmgr：enhanced multi-view graph collaborative recommendation model integrated with large language models [J]. Application Research of Computers, 2027, 44 (2). (2026-10-24). https://doi.org/10.19734/j.issn.1001-3695.2026.06.0157. )

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  • Application Research of Computers Monthly Journal
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    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.

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