Additional Reviewer Assignment by Means of Weighted Association Rules

Cagliero, Luca; Garza, Paolo; Pasini, Andrea; Baralis, Elena

doi:10.1109/tetc.2018.2861214

Cited by 9 publications

(9 citation statements)

References 21 publications

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“…Finally, we would like to design a citation recommender that takes into account the text relationships at the section level as well as the characteristics of the paper content in terms of availability (i.e., open-access vs. restricted). The developed citation recommender system can be integrated into an existing reviewer assignment tool (Cagliero et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Leveraging full-text article exploration for citation analysis

2021

Self Cite

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Scientific articles often include in-text citations quoting from external sources. When the cited source is an article, the citation context can be analyzed by exploring the article full-text. To quickly access the key information, researchers are often interested in identifying the sections of the cited article that are most pertinent to the text surrounding the citation in the citing article. This paper first performs a data-driven analysis of the correlation between the textual content of the sections of the cited article and the text snippet where the citation is placed. The results of the correlation analysis show that the title and abstract of the cited article are likely to include content highly similar to the citing snippet. However, the subsequent sections of the paper often include cited text snippets as well. Hence, there is a need to understand the extent to which an exploration of the full-text of the cited article would be beneficial to gain insights into the citing snippet, considering also the fact that the full-text access could be restricted. To this end, we then propose a classification approach to automatically predicting whether the cited snippets in the full-text of the paper contain a significant amount of new content beyond abstract and title. The proposed approach could support researchers in leveraging full-text article exploration for citation analysis. The experiments conducted on real scientific articles show promising results: the classifier has a 90% chance to correctly distinguish between the full-text exploration and only title and abstract cases.

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Section: Discussionmentioning

confidence: 99%

Leveraging full-text article exploration for citation analysis

2021

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“…A fairly unique approach is presented by Pradhan et al [37] who applied Random Walk with Restart (RWR) to the Reviewer Network, which includes CRs as nodes, while edge weights are defined as a linear combination of several features representing diverse aspects of similarity of two CRs (including similarity of co-authorship profiles, h-index based similarity, similarity of research interests, similarity of publication venues, as well as direct citation and co-citation based similarity). Another unique approach is the one presented by Cagliero et al [3] who tackle a specific version of RAP. In their work, the starting assumption is that the initial assignment of reviewers has already been done and the focus is on finding additional (external) reviewers for submissions with critical assignments (i.e., those that require additional reviews).…”

Section: Rq4: Computational Methods For Assigning / Recommending Revi...mentioning

confidence: 99%

“…The ranking of reviewers is typically based on a custom defined, often topic-based, relevance of each candidate reviewer (CR) for a given submission [44,1,7,12,39]. However, there are also quite distinct approaches for the identification and ranking of reviewers such as the network-based approach by [37] or the weighted association rule mining approach by [3].…”

Section: Rq1: the Overall Approach To Rapmentioning

confidence: 99%

“…Within each of these groups, there is a lot of heterogeneity, as some studies distinguish themselves by the particularities of their focus whereas others by their idiosyncratic methodology. In the former group are, for example, studies by Jecmen et al [11], Cagliero et al [3], and Guo et al [9], where the focus is on ensuring fairness of the review process that goes beyond controlling for explicitly stated COI. In particular, aiming to ensure fairness, Jecmen et al [11] focus on overcoming the practises of untruthful favourable reviews and torpedo reviewing; Cagliero et al [3] focus on finding additional reviewers for submissions with critical assignments (i.e., assignments that are assessed as not fully adequate); whereas Guo et al [9] aim at preventing assignments that correspond to small loops in a bipartite network of authors and reviewers, so that situations where two scholars (who are both authors and reviewers) review each other's papers are avoided.…”

Section: Summary Of Evidencementioning

confidence: 99%

“…This approach is often based on the Latent Dirichlet Allocation (LDA) [2] topic modelling method, in the context of which CRs and submissions are represented as (weighted or unweighted) topic vectors [34,51,36,37,10] or ordered sequences of the identified topics [44] or vectors of most relevant topics identified with LDA [52]. Other topic modelling methods that have been used in the selected studies include Author Topic Model [42], which was adopted in [13,3]; Author-Subject-Topic model, which was proposed and used in [12]; Reviewer Aspect Model [16] used by Yin et al [53], and Latent Semantic Indexing [20] used in [21].…”

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confidence: 99%

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Reviewer Paper Assignment Problem – A Brief Review

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Handbook on ICT in Developing Countries

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The quality of peer review, and consequently the published research, depends to a large extent on the ability to recruit adequate reviewers for submitted papers. However, finding such reviewers is an increasingly difficult task. To alleviate this challenge, numerous solutions have been suggested for automated association of papers with "well matching" reviewers -the task often referred to as reviewer assignment problem (RAP). Yet, to our knowledge, a recent systematic synthesis of the RAP-related literature is missing. To fill this gap and support further RAP-related research, we present a scoping review on computational approaches to RAP. Following the latest methodological guidance for scoping reviews, we have collected and synthesised the literature on RAP published over the last five years (Jan 2016 -Sept 2021). Specifically, the review is focused on the following aspects of RAP research: i) the overall approach to RAP; ii) the criteria used for reviewer selection; iii) the modelling of candidate reviewers and submissions; iv) the computational methods for matching reviewers and submissions; and v) the evaluation methods used for assessing the performance of the proposed solutions. The paper summarises and discusses the findings for each of the aforementioned aspects of RAP research and suggests future research directions.

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Literature Review

Protasiewicz

2023

Studies in Computational Intelligence

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Additional Reviewer Assignment by Means of Weighted Association Rules

Cited by 9 publications

References 21 publications

Leveraging full-text article exploration for citation analysis

Leveraging full-text article exploration for citation analysis

Reviewer Paper Assignment Problem – A Brief Review

Literature Review

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