Please update your browser

You're using a version of Internet Explorer that isn't supported by Questia.
To get a better experience, go to one of these sites and get the latest
version of your preferred browser:

Information vs. Robustness in Rank Aggregation: Models, Algorithms and a Statistical Framework for Evaluation

By Adall, Sibel; Hill, Brandeis et al. | Journal of Digital Information Management, October 2007 | Go to article overview

Information vs. Robustness in Rank Aggregation: Models, Algorithms and a Statistical Framework for Evaluation


Adall, Sibel, Hill, Brandeis, Magdon-Ismail, Malik, Journal of Digital Information Management


ABSTRACT: The rank aggregation problem has been studied extensively in recent years with a focus on how to combine several different rankers to obtain a consensus aggregate ranker. We study the rank aggregation problem from a different perspective: how the individual input rankers impact the performance of the aggregate ranker. We develop a general statistical framework based on a model of how the individual rankers depend on the ground truth ranker. Within this framework, one can generate synthetic data sets and study the performance of different aggregation methods. The individual rankers, which are the inputs to the rank aggregation algorithm, are statistical perturbations of the ground truth ranker. With rigorous experimental evaluation, we study how noise level and the misinformation of the rankers affect the performance of the aggregate ranker. We introduce and study a novel Kendall-tau rank aggregator and a simple aggregator called PrOpt, which we compare to some other well known rank aggregation algorithms such as average, median, CombMNZ and Markov chain aggregators. Our results show that the relative performance of aggregators varies considerably depending on how the input rankers relate to the ground truth.

Categories and Subject Descriptors

H.3.1 [Content Analysis and Indexing] G.3.8 [Statistics and Probability]; Statistical Computing I.2.7 [Natural Language Processing]

General Terms

Ranking algorithms, Search engines, Statistical testing, Rank aggretation

Keywords: Rank aggregation algorithms, Kendall-tau rank aggregator, Rank aggregators

1. Introduction

The rank aggregation problem supposes that a set of objects are ordered by several judges. Typically, the goal is to best represent, according to some measure, the input rankers, independent of the accuracy or correctness of the individual rankers. Such an approach tends to overlook the ultimate goal, which is to obtain a ranking that is "closer" to some ground truth ranking. For Web information retrieval, data in the form of individual rankers is abundant, for example Google, Yahoo, MSN, ..., which are generally based upon ranking algorithms that incorporate information retrieval methods, link based algorithms and other algorithms used to compute the relevance of web pages to a given query. Unfortunately, query results of different rankers differ from each other due to the differences in ranking criteria and the specific algorithms and databases employed by specific rankers. Given this wide variety of differences, what is the best method to aggregate rankers? From a user's perspective, the problem of accessing the appropriate ground truth ranking function for that user (or a group of users) is no longer equivalent to the problem of providing an overall aggregate representation of all the rankers. Rather, one must take into account how the aggregate ranker relates to the ground truth ranker.

To illustrate, imagine two sets of bi-partisan rankers, one representing the left and the other the right points of view. Given these two sets of rankers, is it appropriate to output a consensus ranking that represents all the rankers, in some sense rendering a non-opinion, or should one output a consensus ranking from one of these sets of rankers according to what is more appropriate for a particular user? The answer to this question is dependent on the objective of the consensus ranking: is it to somehow give a summary ranking for the population of rankers (for general queries) or is it to give a ranking that is most useful for the specific user to make actionable choices(with the consideration of user preferences). The impact of the input rankers on the rank aggregation methods can be evaluated given specific knowledge regarding the input rankers.

Problem Statement. In this paper, we present a study of rank aggregation methods as a function of their relationship to the ground truth.

The rest of this article is only available to active members of Questia

Sign up now for a free, 1-day trial and receive full access to:

  • Questia's entire collection
  • Automatic bibliography creation
  • More helpful research tools like notes, citations, and highlights
  • Ad-free environment

Already a member? Log in now.

Notes for this article

Add a new note
If you are trying to select text to create highlights or citations, remember that you must now click or tap on the first word, and then click or tap on the last word.
One moment ...
Project items

Items saved from this article

This article has been saved
Highlights (0)
Some of your highlights are legacy items.

Highlights saved before July 30, 2012 will not be displayed on their respective source pages.

You can easily re-create the highlights by opening the book page or article, selecting the text, and clicking “Highlight.”

Citations (0)
Some of your citations are legacy items.

Any citation created before July 30, 2012 will labeled as a “Cited page.” New citations will be saved as cited passages, pages or articles.

We also added the ability to view new citations from your projects or the book or article where you created them.

Notes (0)
Bookmarks (0)

You have no saved items from this article

Project items include:
  • Saved book/article
  • Highlights
  • Quotes/citations
  • Notes
  • Bookmarks
Notes
Cite this article

Cited article

Style
Citations are available only to our active members.
Sign up now to cite pages or passages in MLA, APA and Chicago citation styles.

(Einhorn, 1992, p. 25)

(Einhorn 25)

1

1. Lois J. Einhorn, Abraham Lincoln, the Orator: Penetrating the Lincoln Legend (Westport, CT: Greenwood Press, 1992), 25, http://www.questia.com/read/27419298.

Cited article

Information vs. Robustness in Rank Aggregation: Models, Algorithms and a Statistical Framework for Evaluation
Settings

Settings

Typeface
Text size Smaller Larger
Search within

Search within this article

Look up

Look up a word

  • Dictionary
  • Thesaurus
Please submit a word or phrase above.
Print this page

Print this page

Why can't I print more than one page at a time?

Full screen

matching results for page

Cited passage

Style
Citations are available only to our active members.
Sign up now to cite pages or passages in MLA, APA and Chicago citation styles.

"Portraying himself as an honest, ordinary person helped Lincoln identify with his audiences." (Einhorn, 1992, p. 25).

"Portraying himself as an honest, ordinary person helped Lincoln identify with his audiences." (Einhorn 25)

"Portraying himself as an honest, ordinary person helped Lincoln identify with his audiences."1

1. Lois J. Einhorn, Abraham Lincoln, the Orator: Penetrating the Lincoln Legend (Westport, CT: Greenwood Press, 1992), 25, http://www.questia.com/read/27419298.

Cited passage

Welcome to the new Questia Reader

The Questia Reader has been updated to provide you with an even better online reading experience.  It is now 100% Responsive, which means you can read our books and articles on any sized device you wish.  All of your favorite tools like notes, highlights, and citations are still here, but the way you select text has been updated to be easier to use, especially on touchscreen devices.  Here's how:

1. Click or tap the first word you want to select.
2. Click or tap the last word you want to select.

OK, got it!

Thanks for trying Questia!

Please continue trying out our research tools, but please note, full functionality is available only to our active members.

Your work will be lost once you leave this Web page.

For full access in an ad-free environment, sign up now for a FREE, 1-day trial.

Already a member? Log in now.