Comparing Partial Rankings

Fagin, Ronald; Kumar, Ravi; Mahdian, Mohammad; Sivakumar, D.; Vee, Erik

doi:10.1137/05063088x

Cited by 122 publications

(137 citation statements)

References 19 publications

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“…This work combines techniques from Ailon et al's recent work on full rank aggregation 6 [5] with Fagin et al's [18,19] recent work on partial rankings. In addition to the methodological contribution (Sections 1.1 and 6), our main results are two approximation algorithms for aggregating partial rankings.…”

Section: Our Results and Comparison With Previous Resultsmentioning

confidence: 99%

“…One of the main drawbacks of considering full rankings is that expecting full ranking information of V from all voters can be too much to ask for [16,17,18,19,20]. In the search engine example, it is unlikely that a search engine would provide a ranking of the entire set V of all web pages matching a given query.…”

Section: Top-m Rankingsmentioning

confidence: 99%

“…Comparison with other choices: Fagin et al [18,19,20] provide a comprehensive picture on how to compare partial rankings In their work, they suggest different natural measures of distance between two partial rankings, extending the well known Kendall-τ and Spearman's footrule [15] metrics on full rankings. Their main contribution is in showing that all the extensions they study belong to a class D of metrics that are equivalent up to global constants, and hence by optimizing or approximating with respect to one we approximate with respect to all.…”

Section: Choice Of Objective Functionmentioning

confidence: 99%

“…Rank aggregation (see [4,10,14,16,17,18,19,20,21] and references therein) is the problem of finding a ranking (permutation) π of a ground set V of n elements combining information from a list π 1 , . .…”

Section: Introductionmentioning

confidence: 99%

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Aggregation of Partial Rankings, p-Ratings and Top-m Lists

2008

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We study the problem of aggregating partial rankings. This problem is motivated by applications such as meta-searching and information retrieval, search engine spam fighting, e-commerce, learning from experts, analysis of population preference sampling, committee decision making and more. We improve recent constant factor approximation algorithms for aggregation of full rankings and generalize them to partial rankings. Our algorithms improved constant factor approximation with respect to all metrics discussed in Fagin et al's recent important work on comparing partial rankings. We pay special attention to two important types of partial rankings: the well-known top-m lists and the more general p-ratings which we define. We provide first evidence for hardness of aggregating them for constant m, p.

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Section: Our Results and Comparison With Previous Resultsmentioning

confidence: 99%

Section: Top-m Rankingsmentioning

confidence: 99%

Section: Choice Of Objective Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Aggregation of Partial Rankings, p-Ratings and Top-m Lists

2008

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“…That means we need to know the distribution of the distance under some common hypotheses about a sample of rankings. In recent years, many new applications appear in different areas including bioinformatics pattern recognition, information retrivial Jurman, Merler, Barla, Paoli, Galea, and Furlanello (2007), Jurman, Riccadonna, Visintainer, andFurlanello (2009), Chan, Yan, Kittler, andMikolajczyk (2015), Fagin, Kumar, Mahdian, Sivakumar, and Vee (2006), Fagin, Kumar, and Sivakumar (2003), etc.…”

Section: Introductionmentioning

confidence: 99%