An Iterative Distance-Based Model for Unsupervised Weighted Rank Aggregation

Rank aggregation is a popular problem that combines different ranked lists from various sources (frequently called voters or judges), and generates a single aggregated list with improved ranking of its items. In this context, a portion of the existing methods attempt to address the problem by treati...

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Veröffentlicht in:	2019 IEEE WIC ACM International Conference on Web Intelligence (WI) S. 358 - 362
Hauptverfasser:	Akritidis, Leonidas, Fevgas, Athanasios, Bozanis, Panayiotis
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	New York, NY, USA ACM 14.10.2019
Schriftenreihe:	ACM Other Conferences
Schlagworte:	Computational modeling Computing methodologies Computing methodologies > Machine learning Convergence distance-based Information systems Information systems > Information retrieval Information systems > Information retrieval > Retrieval models and ranking Information systems > Information retrieval > Retrieval tasks and goals Information systems > Information systems applications Iterative methods Kernel Logic gates Measurement rank aggregation Unsupervised learning unsupervised rank aggregation weighted rank aggregation unsupervised rank aggregation weighted rank aggregation distance-based rank aggregation unsupervised learning
ISBN:	1450369340, 9781450369343
Online-Zugang:	Volltext
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