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Erschienen in:
2007 | OriginalPaper | Buchkapitel
An Unsupervised Learning Algorithm for Rank Aggregation
verfasst von : Alexandre Klementiev, Dan Roth, Kevin Small
Erschienen in: Machine Learning: ECML 2007
Verlag: Springer Berlin Heidelberg
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Many applications in information retrieval, natural language processing, data mining, and related fields require a ranking of instances with respect to a specified criteria as opposed to a classification. Furthermore, for many such problems, multiple established ranking models have been well studied and it is desirable to combine their results into a joint ranking, a formalism denoted as
rank aggregation
. This work presents a novel
unsupervised
learning algorithm for rank aggregation (ULARA) which returns a linear combination of the individual ranking functions based on the principle of rewarding ordering agreement between the rankers. In addition to presenting ULARA, we demonstrate its effectiveness on a data fusion task across ad hoc retrieval systems.