Applied and Computational Engineering

- The Open Access Proceedings Series for Conferences


Proceedings of the 6th International Conference on Computing and Data Science

Series Vol. 64 , 15 May 2024


Open Access | Article

Application of graph modeling and contrast learning in recommender system

Wentao Zhang * 1
1 The University of New South Wales

* Author to whom correspondence should be addressed.

Applied and Computational Engineering, Vol. 64, 65-70
Published 15 May 2024. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Wentao Zhang. Application of graph modeling and contrast learning in recommender system. ACE (2024) Vol. 64: 65-70. DOI: 10.54254/2755-2721/64/20241375.

Abstract

With the wide application of personalized recommender system in various fields, how to improve the accuracy and personalized level of recommender system has become a research hotspot. In this paper, a method of combining graph modeling and contrast learning is proposed to improve the performance of recommendation system by mining complex user project interaction and user preference. We first construct the user-project interaction graph, and extract the features of the graph structure by graph neural network (GNN) . In particular, graph convolution network (GCN) is used to update the node representation, and comparative learning is introduced to optimize the feature representation so as to improve the accuracy and personalization of recommendation. The experimental results show that the proposed method is superior to the traditional method in accuracy, recall and F 1 score. By analyzing the mechanism of combining graph modeling and contrast learning, this paper further expounds the theoretical basis and practical application of improving the performance of recommender system, and points out the limitations of existing methods and the future research direction.

Keywords

Recommendation system, graph modeling, contrast learning, graph convolution network (GCN), feature extraction.

References

1. Vullam, Nagagopiraju, et al. "Multi-Agent Personalized Recommendation System in E-Commerce based on User." 2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC). IEEE, 2023.

2. Ibrahim, Abdelhameed, et al. "A Recommendation System for Electric Vehicles Users Based on Restricted Boltzmann Machine and WaterWheel Plant Algorithms." IEEE Access (2023).

3. da Silva, Felipe Leite, et al. "A systematic literature review on educational recommender systems for teaching and learning: research trends, limitations and opportunities." Education and Information Technologies 28.3 (2023): 3289-3328.

4. Presekal, Alfan, et al. "Attack graph model for cyber-physical power systems using hybrid deep learning." IEEE Transactions on Smart Grid (2023).

5. Pandiaraja, P., K. Muthumanickam, and R. Palani Kumar. "A graph-based model for discovering host-based hook attacks." Smart Technologies in Data Science and Communication: Proceedings of SMART-DSC 2022. Singapore: Springer Nature Singapore, 2023. 1-13.

6. Besta, Maciej, et al. "Graph of thoughts: Solving elaborate problems with large language models." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 38. No. 16. 2024.

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8. Dalmaz, Onat, et al. "One model to unite them all: Personalized federated learning of multi-contrast MRI synthesis." Medical Image Analysis (2024): 103121.

9. Kalantar, Reza, et al. "Non-contrast CT synthesis using patch-based cycle-consistent generative adversarial network (Cycle-GAN) for radiomics and deep learning in the era of COVID-19." Scientific Reports 13.1 (2023): 10568.

10. Ozcan, Samed. "Integration of Dual Color Space and Contrast Learning for Enhancing Satellite Images." (2023).

Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 6th International Conference on Computing and Data Science
ISBN (Print)
978-1-83558-425-5
ISBN (Online)
978-1-83558-426-2
Published Date
15 May 2024
Series
Applied and Computational Engineering
ISSN (Print)
2755-2721
ISSN (Online)
2755-273X
DOI
10.54254/2755-2721/64/20241375
Copyright
15 May 2024
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated