A Three-Layer Network Model for Reviewer Recommendation

Xu, Yunhong; Du, Yuyue

doi:10.1109/bife.2013.115

Cited by 2 publications

(1 citation statement)

References 20 publications

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“…Liu et al [5] used semantic similarity scores between reviewers and manuscripts obtained through LDA analysis as the weight of each link and employed a random walk with restart (RWR) model on the network [32] to balance the expertise and authority of recommended reviewers. Except for the two kinds of basic models, researchers also built networks with more diverse structures, such as three-layer networks [33,34] and multiple networks. [35] Based on the constructed network, one can also utilize node ranking methods [36,37] or topology identification methods [38] to make recommendations.…”

Section: Non-text-based Methodsmentioning

confidence: 99%

A multilayer network diffusion-based model for reviewer recommendation

Huang 黄,

Xu 徐,

Cai 蔡

et al. 2024

Chinese Phys. B

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With the rapid growth of manuscript submissions, finding eligible reviewers for every submission has become a heavy task. Recommender systems are powerful tools developed in computer science and information science to deal with this problem. However, most existing approaches resort to text mining techniques to match manuscripts with potential reviewers, which require high-quality textual information to perform well. In this paper, we propose a reviewer recommendation algorithm based on a network diffusion process on a scholar-paper multilayer network, with no requirement for textual information. The network incorporates the relationship of scholar-paper pairs, the collaboration among scholars, and the bibliographic coupling among papers. Experimental results show that our proposed algorithm outperforms other state-of-the-art recommendation methods that use graph random walk and matrix factorization and methods that use machine learning and natural language processing, with improvements of over 7.62% in Recall, 5.66% in Hit Rate, and 47.53% in Ranking Score. Our work sheds light on the effectiveness of multilayer network diffusion-based methods in the reviewer recommendation problem, which will help to facilitate the peer-review process and promote information retrieval research in other practical scenes.

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Section: Non-text-based Methodsmentioning

confidence: 99%

A multilayer network diffusion-based model for reviewer recommendation

Huang 黄,

Xu 徐,

Cai 蔡

et al. 2024

Chinese Phys. B

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Scholarly recommendation systems: a literature survey

Zhang,

Patra,

Yaseen

et al. 2023

Knowl Inf Syst

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A scholarly recommendation system is an important tool for identifying prior and related resources such as literature, datasets, grants, and collaborators. A well-designed scholarly recommender significantly saves the time of researchers and can provide information that would not otherwise be considered. The usefulness of scholarly recommendations, especially literature recommendations, has been established by the widespread acceptance of web search engines such as CiteSeerX, Google Scholar, and Semantic Scholar. This article discusses different aspects and developments of scholarly recommendation systems. We searched the ACM Digital Library, DBLP, IEEE Explorer, and Scopus for publications in the domain of scholarly recommendations for literature, collaborators, reviewers, conferences and journals, datasets, and grant funding. In total, 225 publications were identified in these areas. We discuss methodologies used to develop scholarly recommender systems. Content-based filtering is the most commonly applied technique, whereas collaborative filtering is more popular among conference recommenders. The implementation of deep learning algorithms in scholarly recommendation systems is rare among the screened publications. We found fewer publications in the areas of the dataset and grant funding recommenders than in other areas. Furthermore, studies analyzing users’ feedback to improve scholarly recommendation systems are rare for recommenders. This survey provides background knowledge regarding existing research on scholarly recommenders and aids in developing future recommendation systems in this domain.

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A Three-Layer Network Model for Reviewer Recommendation

Cited by 2 publications

References 20 publications

A multilayer network diffusion-based model for reviewer recommendation

A multilayer network diffusion-based model for reviewer recommendation

Scholarly recommendation systems: a literature survey

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