An Intelligent Endorsement Technique in a Big Data

Authors

  • Bhadane Ashwini Dilip Research Scholar
  • Varsha H Patil Research Scholar

Keywords:

Proposals Assignment, Reviewers Assignment, Proposals Grouping, Decision Making

Abstract

The obligation problem is a essential problem when it comes to
assigning referees to research proposals. Matching of reviewers and
research proposals affects the review quality. The number of proposals
is continuously increasing day by day and hence they fail to satisfy the
practical needs. This paper proposes an approach where the proposals
will be grouped first and then will be assigned to appropriate referees.
The basic idea is to identify valid proposals and reviewers, classify them,
partition the proposals into groups and assign reviewers to proposal
groups. A system is been developed based on the proposed approach
for assigning the proposals to reviewers.

Author Biographies

Bhadane Ashwini Dilip, Research Scholar

, Department of Computer
Engineering, Matoshri College of Engineering,
Nashik, India.

Varsha H Patil, Research Scholar

Department of Computer
Engineering, Matoshri College of Engineering,
Nashik, India.

References

Lian JW, Mattei N, Noble R. The Conference Paper

Assignment Problem: Using Order Weighted Averages

to Assign Indivisible Goods. 2017.

Li L, Wang Y, Liu G et al. Context-Aware Reviewer

Assignment for Trust Enhanced Peer Re-view. 2015.

Das GS, Gken T. A fuzzy approach for the reviewer

assignment problem. 2014.

Shon HS, Han SH, Kim KA et al. Proposal reviewer

recommendation system based on big data for a

national research management institute. 2016.

Liu D, Xu W, Du W et al. How to Choose Appropriate

Experts for Peer Review. 2015.

Yue M, Tian K, Ma T. An Accurate and Impartial Expert

Assignment Method for Scientific Project Review. 2017.

Janak SL, Taylor, Floudas CA, Burka B et al. Novel

and Effective Integer Optimization Approach for the

NSF Panel-Assignment Problem: A Multiresource

and Preference-Constrained Generalized Assignment

Problem. 2006.

Xu Y, Ma J, Sun Y et al. A decision support approach

for assigning reviewers to proposals. 2010.

Wu S, Hou L, Bhowmick SS et al. PISTIS:A Conflict of

Interest Declaration and Detection System for Peer

Review Management. 2010.

Andrew D. Expertise modeling for matching papers

with reviewers. In Proceedings of the 13th ACM SIGKDD

International conference on knowledge discovery and

data mining. 2007.

Deep K, Das KN. Quadratic approximation based hybrid

genetic algorithm for function optimization. Applied

Mathematics and Compu-tation. 2008.

Amendola G, Dodaro C, Leone N et al. On the application

of answer set programming to the conference paper

assignment problem. 2016.

Aziz H, Lev O, Mattei N et al. Strategyproof peer

selection: Mechanisms, analyses and experiments.

Price S, Flach PA. Computational support for academic

peer review: A perspective from articial intelligence.

Published

2019-12-20