Download pdf
GLEM-Rec—A Research on Cross-modal Recommendation Framework Based on Semantic-Graph Structure
- 1 School of Information Management & Engineering, Shanghai University of Finance and Economics, Shanghai, China, 200000
* Author to whom correspondence should be addressed.
Abstract
This paper proposes GLEM-Rec, a cross-modal recommendation framework integrating large language models with graph neural networks, effectively addressing three major challenges in traditional recommendation systems: semantic-graph structure feature alignment, long-tail item recommendation, and explainability. The framework consists of five core modules: semantic feature extractor, heterogeneous data processor, heterogeneous GNN integrator, adaptive trainer, and explainable recommendation generator, achieving complementary advantages between LLM's deep semantic understanding and GNN's high-order relationship modeling. Through multi-objective optimization strategies, GLEM-Rec achieves a balance between prediction accuracy, recommendation diversity, personalization, and long-tail coverage. Experiments based on the Movies Dataset demonstrate that this framework significantly outperforms existing methods, achieving an RMSE of 0.9122, coverage rate of 0.7723, and long-tail item recommendation performance of 0.9851, comprehensively surpassing traditional baseline models. System ablation experiments confirm the necessity and effectiveness of each functional module, validating the critical contribution of semantic and graph structure feature collaboration to recommendation system performance. This research not only provides new theoretical support for cross-modal recommendation systems but also offers effective technical solutions for key challenges in recommendation system practice.
Keywords
Recommendation Systems, GNN, LLM, Explainability, Contrastive Learning
[1]. Wu C., Wu F., Qi T., et al. Mm-rec: multimodal news recommendation [J]. arXiv preprint arXiv:2104.07407, 2021.
[2]. Hong Y., Li H., Wang X., Lin C. (2020). DEAMER: A Deep Exposure-Aware Multimodal Content-Based Recommendation System. Database Systems for Advanced Applications. DASFAA 2020. Lecture Notes in Computer Science vol 12114. Springer, Cham. https://doi.org/10.1007/978-3-030-59419-0_38
[3]. Fan W., Ma Y., Li Q., et al. Graph neural networks for social recommendation [C]// The world wide web conference. 2019: 417-426.
[4]. Wu Q., Zhang H., Gao X., et al. Dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems [C]// The world wide web conference. 2019: 2091-2102.
[5]. Liu Q., Zhu J., Yang Y., et al. Multimodal pretraining, adaptation, and generation for recommendation: A survey [C]// Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2024: 6566-6576.
[6]. Xu C., Guan Z., Zhao W., et al. Recommendation by users’ multimodal preferences for smart city applications [J]. IEEE Transactions on Industrial Informatics, 2020, 17(6): 4197-4205.
[7]. Liu Q., Hu J., Xiao Y., et al. Multimodal recommender systems: A survey [J]. ACM Computing Surveys, 2024, 57(2): 1-17.
[8]. Jiang J., Zhang M. Overspinning a rotating black hole in semiclassical gravity with type-A trace anomaly [J]. The European Physical Journal C, 2023, 83(8): 687.
[9]. Hongzhi Wen, Jiayuan Ding, Wei Jin, Yiqi Wang, Yuying Xie, and Jiliang Tang. (2022). Graph Neural Networks for Multimodal Single-Cell Data Integration. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22). Association for Computing Machinery, New York, NY, USA, 4153–4163. https://doi.org/10.1145/3534678.3539213
[10]. Reimers N., Gurevych I. Sentence-bert: Sentence embeddings using siamese bert-networks[J]. arXiv preprint arXiv:1908.10084, 2019.
[11]. Hamilton W., Ying Z., Leskovec J. Inductive representation learning on large graphs [J]. Advances in neural information processing systems, 2017, 30.
Cite this article
Zhang,Y. (2025). GLEM-Rec—A Research on Cross-modal Recommendation Framework Based on Semantic-Graph Structure. Applied and Computational Engineering,165,1-10.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of CONF-MLA 2025 Symposium: Intelligent Systems and Automation: AI Models, IoT, and Robotic Algorithm
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).