A Keyword-based Scholar Recommendation Framework for Biomedical Literature

Yang, Fen; Zhu, Jia; Lun, Jiaqi; Zheng, Zetao; Tang, Yong; Wu, Jian

doi:10.1109/cscwd.2018.8465142

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…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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“…Current recommender systems that deal with biomedical data, target different tasks, such as recommending ontologies to annotate biomedical text [39], model biological processes [40], recommending drugs to target SARS-CoV-2 regarding the COVID-19 pandemic [41], recommending entities of potential interest to specific researchers [12], or even recommending articles to expand existing biomedical datasets [42]. There is also a focus on recommending articles and venues to researchers to limit their search space [43], [44], for instance, by performing keyword-based recommendation [45]. Further, there is a significant amount of work done on biomedical KG completion [46]- [48], including trying to depend less on domain-specific labeling and going through a minimum supervision route that can scale with the volume of literature available [49].…”

Section: Related Workmentioning

confidence: 99%

Biomedical Relation Extraction With Knowledge Graph-Based Recommendations

Sousa

Couto

2022

IEEE J. Biomed. Health Inform.

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Biomedical Relation Extraction (RE) systems identify and classify relations between biomedical entities to enhance our knowledge of biological and medical processes. Most state-of-the-art systems use deep learning approaches, mainly to target relations between entities of the same type, such as proteins or pharmacological substances. However, these systems are mostly restricted to what they directly identify on the text and ignore specialized domain knowledge bases, such as ontologies, that formalize and integrate biomedical information typically structured as direct acyclic graphs. On the other hand, Knowledge Graph (KG)-based recommendation systems already showed the importance of integrating KGs to add additional features to items. Typical systems have users as people and items that can range from movies to books, which people saw or read and classified according to their satisfaction rate. This work proposes to integrate KGs into biomedical RE through a recommendation model to further improve their range of action. We developed a new RE system, named K-BiOnt, by integrating a baseline state-of-theart deep biomedical RE system with an existing KG-based recommendation state-of-the-art system. Our results show that adding recommendations from KG-based recommendation improves the system's ability to identify true relations that the baseline deep RE model could not extract from the text. The code supporting this system is available at https://github.com/lasigeBioTM/K-BiOnt.

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“…Tian et al [53] proposed a weighted PersonalRank algorithm to recommend activities of interest to a specific volunteer on a sparse dataset. Yang et al [54] proposed a keyword-based scholar recommendation framework that constructed a bipartite graph by extracting keywords from abstracts. They used a bipartite graph-based PersonalRank algorithm to rank scholars by using a sparse dataset.…”

Section: B Bipartite Graph Modelmentioning

confidence: 99%

A Course Teacher Recommendation Method Based on an Improved Weighted Bipartite Graph and Slope One

2022

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The quality of course teaching is directly related to education quality. Many scholars have attempted to identify the associations between course-teaching quality and teachers' characteristics, such as educational background, degree, professional title, age, teaching age, job burnout, and academic research. However, because these characteristics are mostly evolvable, research findings are inconsistent. Therefore, we attempted to identify the association between teaching styles that reflect teachers' stable psychological quality, Technological Pedagogical Content Knowledge (TPACK), and teaching quality. To this end, we first collected data from three different disciplines at a university using the constructed teaching quality, TPACK, and course difficulty questionnaires, together with the TSTI scale proposed by Grigorenko and Sternberg. We constructed three matrices with different sparsities as experimental datasets using teachers with the teaching style and PTACK attributes, courses with the course difficulty attribute, and teaching quality. We then constructed a weighted bipartite graph with the teachers and courses in the matrix as nodes and the teaching quality divided by course difficulty as the weights of the edges. We proposed an improved Slope One algorithm based on a weighted bipartite graph to scientifically predict teachers' teaching quality in untaught courses. Finally, we constructed a TOP-N recommendation model for course teachers that combined teaching style and TPACK features to achieve accurate recommendations for course teachers. The experiments show that our proposed solution is feasible and that the algorithmic model is effective. Therefore, we developed a scientific method to improve the quality of university course teaching.

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A Keyword-based Scholar Recommendation Framework for Biomedical Literature

Cited by 6 publications

References 10 publications

A multilayer network diffusion-based model for reviewer recommendation

A multilayer network diffusion-based model for reviewer recommendation

Biomedical Relation Extraction With Knowledge Graph-Based Recommendations

A Course Teacher Recommendation Method Based on an Improved Weighted Bipartite Graph and Slope One

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