Rank aggregation using latent-scale distance-based models

Research output: Contribution to journalArticles

3 Citations (Scopus)

Abstract

Rank aggregation aims at combining rankings of a set of items assigned by a sample of rankers to generate a consensus ranking. A typical solution is to adopt a distance-based approach to minimize the sum of the distances to the observed rankings. However, this simple sum may not be appropriate when the quality of rankers varies. This happens when rankers with different backgrounds may have different cognitive levels of examining the items. In this paper, we develop a new distance-based model by allowing different weights for different rankers. Under this model, the weight associated with a ranker is used to measure his/her cognitive level of ranking of the items, and these weights are unobserved and exponentially distributed. Maximum likelihood method is used for model estimation. Extensions to the cases of incomplete rankings and mixture modeling are also discussed. Empirical applications demonstrate that the proposed model produces better rank aggregation than those generated by Borda and the unweighted distance-based models. Copyright © 2018 Springer Science+Business Media, LLC, part of Springer Nature.
Original languageEnglish
Pages (from-to)335-349
JournalStatistics and Computing
Volume29
Issue number2
Early online dateApr 2018
DOIs
Publication statusPublished - 2019

Citation

Yu, P. L. H., & Xu, H. (2019). Rank aggregation using latent-scale distance-based models. Statistics and Computing, 29(2), 335-349. doi: 10.1007/s11222-018-9811-9

Keywords

  • Ranking data
  • Latent-scale distance-based model
  • Rank aggregration
  • Incomplete ranking

Fingerprint Dive into the research topics of 'Rank aggregation using latent-scale distance-based models'. Together they form a unique fingerprint.