LoboVault Home

Improving peer review with ACORN : Ant Colony Optimization algorithm for Reviewer's Network

LoboVault

Please use this identifier to cite or link to this item: http://hdl.handle.net/1928/13085

Improving peer review with ACORN : Ant Colony Optimization algorithm for Reviewer's Network

Show full item record

Title: Improving peer review with ACORN : Ant Colony Optimization algorithm for Reviewer's Network
Author: Flynn, Mark
Advisor(s): Moses, Melanie
Committee Member(s): Luger, George
Greene, Kshanti
Department: University of New Mexico. Dept. of Computer Science
Subject: Ant Colony Optimization
Peer review
LC Subject(s): Peer review--Statistical methods.
Ant algorithms.
Degree Level: Masters
Abstract: Peer review, our current system for determining which papers to accept and which to reject by journals and conferences, has limitations that impair the quality of scientific communication. Under the current system, reviewers have only a limited amount of time to devote to evaluating papers and each paper receives an equal amount of attention regardless of how good the paper is. We propose to implement a new system for conference peer review based on ant colony optimization (ACO) algorithms. In our model, each reviewer has a set of ants that goes out and finds articles. The reviewer assesses the paper that the ant brings according to the criteria specified by the conference organizers and the ant deposits pheromone that is proportional to the quality of the review. Each subsequent ant then samples the pheromones and probabilistically selects the next article based on the strength of the pheromones. We used an agent-based model to determine if an ACO-based paper selection system will direct reviewers’ attention to the best articles and if the average quality of papers increases with each round of reviews. We also conducted an experiment in conjunction with the 2011 UNM Computer Science Graduate Student Association conference and compared the results with our simulation. To assess the usefulness of our approach, we compared our algorithm to a greedy algorithm that always takes the best un-reviewed paper and a latent factor analysis recommender-based system. We found that the ACO-based algorithm was better than either of the greedy or recommender algorithms at directing users’ attention to the better papers.
Graduation Date: July 2011
URI: http://hdl.handle.net/1928/13085


Files in this item

Files Size Format View
Flynn_masters_thesis_final1.pdf 4.554Mb PDF View/Open

This item appears in the following Collection(s)

Show full item record

UNM Libraries

Search LoboVault


Browse

My Account