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Review allocation method of fusing academic expertise and social network

A technology of social network and distribution method, applied in the field of computer application technology, thesis/project review management application, can solve the problem of not considering objectivity, etc., and achieve the effect of balanced review and distribution

Active Publication Date: 2017-09-22
BEIJING INSTITUTE OF TECHNOLOGYGY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0009] The present invention aims to solve the defect that the existing review assignment method does not consider the academic communication between the author / applicant of the paper / project and the experts, which affects the objectivity of review assignment, and proposes a review assignment method that integrates academic expertise and social network

Method used

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  • Review allocation method of fusing academic expertise and social network
  • Review allocation method of fusing academic expertise and social network

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Experimental program
Comparison scheme
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Embodiment 1

[0091] This embodiment elaborates in detail the process of "a review assignment method integrating academic expertise and social network" of the present invention applied to the review assignment process of an academic conference in the management of paper review.

[0092] figure 1 It is the algorithm flowchart of this method and the flowchart of this embodiment; As can be seen from the figure, this method comprises the following steps:

[0093] Step A: Given 4 labels, 10 labeled papers and 7 experts, each paper is jointly completed by 2 authors, and the label set, labeled paper collection and expert collection, and the author collection of the paper collection are established; Get the number of papers and experts, label similarity matrix and paper-expert label similarity;

[0094] Specific to this embodiment, the label set T={a,b,c,d} is established, and the labeled paper collection P={p 0 ,p 1 ,p 2 ,p 3 ,p 4 ,p 5 ,p 6 ,p 7 ,p 8 ,p 9}, the labeled expert set R={r ...

Embodiment 2

[0132] According to the parameters described in Embodiment 1, this embodiment specifically illustrates the calculation process of the paper / project-expert cooperation distance defined in Step 2 of the present invention and the paper-expert cooperation distance in Step B of Embodiment 1.

[0133] The specific process is: social network G=(V,E), node set |V|=200, and expert set Paper Author Collection Edge set |E|=5000; According to the definition, the author-expert cooperation distance array of the paper is calculated

[0134]

[0135] According to the definition, the paper-expert cooperation distance D(p,r) is calculated:

[0136]

[0137] If we do not consider the paper-expert cooperation distance, but only consider maximizing the paper-expert label similarity for review assignment, the experts are likely to affect the objectivity of the paper review due to subjective factors; among them, the paper-expert labels are similar Degree is obtained by S (p, r) of embodime...

Embodiment 3

[0140] According to the parameters described in Embodiment 1, this embodiment specifically elaborates the paper / project-expert matching degree defined in step 5 of the present invention, the optimization goal defined in step 7, and the paper-expert matching in step E of embodiment 1. degree, the optimization goal determined in step G.

[0141] Specifically in this embodiment, step E: the formula for calculating the paper-expert matching degree is:

[0142]

[0143] Step G: The optimization objective for review allocation is:

[0144] max sum Matching

[0145] st.for 0≤i,j<10 and i≠j,0≤s,t<3 and s≠t,that

[0146]

[0147]

[0148]

[0149]

[0150] for R a , that|R a |==2

[0151]The optimization goal established in this embodiment is to maximize the matching degree of the paper-expert under the premise of satisfying the constraint conditions and balanced distribution; to maximize the matching degree of the paper-expert, it is necessary to maximize the label...

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Abstract

The invention discloses a review allocation method of fusing academic expertise and a social network, and belongs to the field of computer application technology. According to the method, a label set, a paper / project set and an expert set are established, and label similarity degrees of papers / projects and experts are calculated according to a label similarity degree matrix; the social network is modeled based on a paper / project cooperation database, and cooperation distances of the papers / projects and the experts are calculated; an allocation array of the papers / projects and the experts is established, the paper set is traversed, and review allocation is carried out in sequence according to an algorithm of maximum matching degree priority and minimum difference adjustment; and an optimal allocation result and the sum of matching degrees, the sum of the label similarity degrees and the sum of the cooperation distances thereof are output. Meeting constraint conditions that authors / applicants of the papers / projects and the experts do not in cooperation, do not in teacher-student relations and do not belong to the same institutions, the method can realize balanced review allocation, maximize the sum of the label similarity degrees and the sum of the cooperation distances of review allocation, and ensure the objectivity, the fairness and the impartiality of the review result.

Description

technical field [0001] The invention relates to a review assignment method integrating academic expertise and social network, and belongs to the application fields of computer application technology and thesis / project review management. Background technique [0002] At present, the country and universities are paying more and more attention to academic and scientific research, and the number of papers and project applications is increasing year by year. Assigning papers / projects to appropriate reviewers within a limited time has become a problem that plagues conferences, journals, and project organizers. The review opinions of review experts are the direct basis for paper acceptance or project award. Therefore, ensuring the fairness and authority of review results has always been the focus of the organizers. In the face of a large number of papers / projects and heavy organizational work, it has become a trend to use computer technology for review assignments, but most of the ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/10
CPCG06Q10/103
Inventor 曹朝曲大成李凯霞
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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