An efficient ontology-based topic-specific article recommendation model for best-fit reviewers

Chughtai, Gohar Rehman; Lee, Jia; Shahzadi, Mahnoor; Kabir, Asif; Hassan, Muhammad Arshad Shehzad

doi:10.1007/s11192-019-03261-2

Cited by 11 publications

(6 citation statements)

References 24 publications

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“…As already indicated in our introduction, we envision further applications of our method. First, our venue embeddings could be used in a recommendation scenario, e.g., similar as in Chughtai et al (2020). Second, it would be interesting to explore whether topic space trajectories can be extrapolated into the future.…”

Section: Discussionmentioning

confidence: 99%

“…A very prominent topic model, called Latent Semantic Analysis (LSA) (Deerwester et al 1990), is based on the singular value decomposition of the word-document matrix. This method has been proven to work well for various natural language processing tasks, e.g., in Steinberger and Křišt'an (2007), in Pu and Yang (2006) or for recommendations of scientific articles in Chughtai et al (2020). Nonetheless, we decided against its application in our work.…”

Section: Document Representation In Topic Spacementioning

confidence: 99%

“…The classic topic model is Latent Semantic Analysis (LSA) (Deerwester et al 1990), which performs a singular value decomposition of this matrix. It has been used for various text mining tasks, e.g., recently in recommendations of scientific articles (Chughtai et al 2020). Similarly to LSA, non-negative matrix factorization (NMF) (Lee and Seung 1999) decomposes a matrix into two factors, however with the constraint that all parts which contribute to the reconstruction of the input matrix must be positive.…”

Section: Related Workmentioning

confidence: 99%

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Topic space trajectories

2021

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The annual number of publications at scientific venues, for example, conferences and journals, is growing quickly. Hence, even for researchers it becomes harder and harder to keep track of research topics and their progress. In this task, researchers can be supported by automated publication analysis. Yet, many such methods result in uninterpretable, purely numerical representations. As an attempt to support human analysts, we present topic space trajectories, a structure that allows for the comprehensible tracking of research topics. We demonstrate how these trajectories can be interpreted based on eight different analysis approaches. To obtain comprehensible results, we employ non-negative matrix factorization as well as suitable visualization techniques. We show the applicability of our approach on a publication corpus spanning 50 years of machine learning research from 32 publication venues. In addition to a thorough introduction of our method, our focus is on an extensive analysis of the results we achieved. Our novel analysis method may be employed for paper classification, for the prediction of future research topics, and for the recommendation of fitting conferences and journals for submitting unpublished work. An advantage in these applications over previous methods lies in the good interpretability of the results obtained through our methods.

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Section: Discussionmentioning

confidence: 99%

Section: Document Representation In Topic Spacementioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Topic space trajectories

2021

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“…The system needs to be automatic for more fair, objective, and expedient review assignments. Despite this requirement, the number of empirical studies submitted to the RAP for journal papers (Biswas & Hasan, 2007;Karimzadehgan & Zhai, 2009;Daud et al, 2010;Andrade-Navarro et al, 2012;Wang et al, 2013;Protasiewicz, 2014;Yin et al, 2016;Peng et al, 2017;Jin et al, 2018aJin et al, , 2018bZhao et al, 2018;Duan et al, 2019;Chughtai et al, 2019;Tan et al, 2021;Hoang et al, 2021) is less than that for conference papers.…”

Section: Rap For Journal Papersmentioning

confidence: 99%

Reviewer Assignment Problem: A Systematic Review of the Literature

Meltem¹,

Yanık

Amasyalı

2023

jair

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Appropriate reviewer assignment significantly impacts the quality of proposal evaluation, as accurate and fair reviews are contingent on their assignment to relevant reviewers. The crucial task of assigning reviewers to submitted proposals is the starting point of the review process and is also known as the reviewer assignment problem (RAP). Due to the obvious restrictions of manual assignment, journal editors, conference organizers, and grant managers demand automatic reviewer assignment approaches. Many studies have proposed assignment solutions in response to the demand for automated procedures since 1992. The primary objective of this survey paper is to provide scholars and practitioners with a comprehensive overview of available research on the RAP. To achieve this goal, this article presents an in-depth systematic review of 103 publications in the field of reviewer assignment published in the past three decades and available in the Web of Science, Scopus, ScienceDirect, Google Scholar, and Semantic Scholar databases. This review paper classified and discussed the RAP approaches into two broad categories and numerous subcategories based on their underlying techniques. Furthermore, potential future research directions for each category are presented. This survey shows that the research on the RAP is becoming more significant and that more effort is required to develop new approaches and a framework.

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“…A very prominent topic model, called Latent Semantic Analysis (LSA) (Deerwester et al, 1990), is based on the singular value decomposition of the worddocument matrix. This method has been proven to work well for various natural language processing tasks, e.g., in Steinberger and Křišt'an (2007), in Pu and Yang (2006) or for recommendations of scientific articles in Chughtai et al (2020). Nonetheless, we decided against its application in our work.…”

Section: Document Representation In Topic Spacementioning

confidence: 99%

Topic Space Trajectories: A case study on machine learning literature

Schäfermeier,

Stumme,

Hanika

2020

Preprint

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The annual number of publications at scientific venues, for example, conferences and journals, is growing quickly. Hence, even for researchers it becomes harder and harder to keep track of research topics and their progress. In this task, researchers can be supported by automated publication analysis. Yet, many such methods result in uninterpretable, purely numerical representations.As an attempt to support human analysts, we present topic space trajectories, a structure that allows for the comprehensible tracking of research topics. We demonstrate how these trajectories can be interpreted based on eight different analysis approaches. To obtain comprehensible results, we employ non-negative matrix factorization as well as suitable visualization techniques. We show the applicability of our approach on a publication corpus spanning 50 years of machine learning research from 32 publication venues. Our novel analysis method may be employed for paper classification, for the prediction of future research topics, and for the recommendation of fitting conferences and journals for submitting unpublished work.

show abstract

An efficient ontology-based topic-specific article recommendation model for best-fit reviewers

Cited by 11 publications

References 24 publications

Topic space trajectories

Topic space trajectories

Reviewer Assignment Problem: A Systematic Review of the Literature

Topic Space Trajectories: A case study on machine learning literature

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