Thesis Reviewer Recommendation Based on Multi-Graph Neural Networks

Wang Tao, Fu Peng, Zhang Jing, Chen Kaixuan
Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing, PMLR 245:48-56, 2024.

Abstract

Thesis review is a crucial step in ensuring the quality of academic theses, and accurately recom-mending reviewers for theses is currently a problem that needs to be addressed. When reviewer information is incomplete, it is difficult to achieve good recommendation results. This paper pro-poses a Multi-Graph Neural Network algorithm for review reviewer recommendation in thesis re-view. Based on the keyword-reviewer bipartite graph, a graph neural network model is constructed. By utilizing graph neural networks, high-order relationships between reviews and keywords can be explored, enabling the discovery of reviews’ implicit research interests and expanding their re-search interests to some extent. Additionally, incorporating keyword-keyword interaction graphs and review-review interaction graphs allows for information exchange operations in the two graphs separately, enhancing the representation of keywords and reviews. We conducted experiments on real thesis reviews and compared the proposed algorithm with other recommendation algorithms. The results show that the proposed algorithm achieves favorable results across various evaluation metrics, demonstrating the effectiveness of the algorithm presented in this paper.

Cite this Paper

BibTeX
@InProceedings{pmlr-v245-tao24a, title = {Thesis Reviewer Recommendation Based on Multi-Graph Neural Networks}, author = {Tao, Wang and Peng, Fu and Jing, Zhang and Kaixuan, Chen}, booktitle = {Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing}, pages = {48--56}, year = {2024}, editor = {Nianyin, Zeng and Pachori, Ram Bilas}, volume = {245}, series = {Proceedings of Machine Learning Research}, month = {26--28 Apr}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v245/main/assets/tao24a/tao24a.pdf}, url = {https://proceedings.mlr.press/v245/tao24a.html}, abstract = {Thesis review is a crucial step in ensuring the quality of academic theses, and accurately recom-mending reviewers for theses is currently a problem that needs to be addressed. When reviewer information is incomplete, it is difficult to achieve good recommendation results. This paper pro-poses a Multi-Graph Neural Network algorithm for review reviewer recommendation in thesis re-view. Based on the keyword-reviewer bipartite graph, a graph neural network model is constructed. By utilizing graph neural networks, high-order relationships between reviews and keywords can be explored, enabling the discovery of reviews’ implicit research interests and expanding their re-search interests to some extent. Additionally, incorporating keyword-keyword interaction graphs and review-review interaction graphs allows for information exchange operations in the two graphs separately, enhancing the representation of keywords and reviews. We conducted experiments on real thesis reviews and compared the proposed algorithm with other recommendation algorithms. The results show that the proposed algorithm achieves favorable results across various evaluation metrics, demonstrating the effectiveness of the algorithm presented in this paper. } }
Endnote
%0 Conference Paper %T Thesis Reviewer Recommendation Based on Multi-Graph Neural Networks %A Wang Tao %A Fu Peng %A Zhang Jing %A Chen Kaixuan %B Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing %C Proceedings of Machine Learning Research %D 2024 %E Zeng Nianyin %E Ram Bilas Pachori %F pmlr-v245-tao24a %I PMLR %P 48--56 %U https://proceedings.mlr.press/v245/tao24a.html %V 245 %X Thesis review is a crucial step in ensuring the quality of academic theses, and accurately recom-mending reviewers for theses is currently a problem that needs to be addressed. When reviewer information is incomplete, it is difficult to achieve good recommendation results. This paper pro-poses a Multi-Graph Neural Network algorithm for review reviewer recommendation in thesis re-view. Based on the keyword-reviewer bipartite graph, a graph neural network model is constructed. By utilizing graph neural networks, high-order relationships between reviews and keywords can be explored, enabling the discovery of reviews’ implicit research interests and expanding their re-search interests to some extent. Additionally, incorporating keyword-keyword interaction graphs and review-review interaction graphs allows for information exchange operations in the two graphs separately, enhancing the representation of keywords and reviews. We conducted experiments on real thesis reviews and compared the proposed algorithm with other recommendation algorithms. The results show that the proposed algorithm achieves favorable results across various evaluation metrics, demonstrating the effectiveness of the algorithm presented in this paper.
APA
Tao, W., Peng, F., Jing, Z. & Kaixuan, C.. (2024). Thesis Reviewer Recommendation Based on Multi-Graph Neural Networks. Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing, in Proceedings of Machine Learning Research 245:48-56 Available from https://proceedings.mlr.press/v245/tao24a.html.

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