A Heuristic Approach for Ranking Items Based on Inputs from Multiple Experts

A Heuristic Approach for Ranking Items Based on Inputs from Multiple Experts

Dong Xu, Nazrul I. Shaikh
Copyright: © 2018 |Pages: 22
DOI: 10.4018/IJISSC.2018070101
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Abstract

This article describes how rank aggregation focuses on synthesizing a single ranked list based on rankings supplied by multiple judges. Such aggregations are widely applied in the areas of information retrieval, web search, and data mining. The problem of rank aggregation has been shown to be NP-hard and this article presents a heuristic approach to create an aggregated ranking score for all items on the lists. The proposed heuristic is scalable and performs. A computational study, as well as a real-life study involving the ranking of 147 engineering colleges in the US is presented to elucidate the performance. The authors' key finding is that the quality of the solution is sensitive to (a) the number of judges available to rank, (b) how the items are assigned to judges, and (c) how consistent/inconsistent the judges are. All these factors are generally considered exogenous in most of the rank aggregation algorithms in extant literature.
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2. Background

The solution approaches to the rank aggregation problem are generally classified as axiomatic, stochastic, supervised learning, and heuristic.

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