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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| Published in: | 2019 IEEE WIC ACM International Conference on Web Intelligence (WI) pp. 358 - 362 |
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| Language: | English |
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New York, NY, USA
ACM
14.10.2019
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| ISBN: | 1450369340, 9781450369343 |
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| Abstract | 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 treating all voters equally. Nevertheless, several related works proved that the careful and effective assignment of different weights to each voter leads to enhanced performance. In this article, we introduce an unsupervised algorithm for learning the weights of the voters for a specific topic or query. The proposed method is based on the fact that if a voter has submitted numerous elements which have been placed in high positions in the aggregated list, then this voter should be treated as an expert, compared to the voters whose suggestions appear in lower places or do not appear at all. The algorithm iteratively computes the distance of each input list with the aggregated list and modifies the weights of the voters until all weights converge. The effectiveness of the proposed method is experimentally demonstrated by aggregating input lists from six TREC conferences. |
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| AbstractList | 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 treating all voters equally. Nevertheless, several related works proved that the careful and effective assignment of different weights to each voter leads to enhanced performance. In this article, we introduce an unsupervised algorithm for learning the weights of the voters for a specific topic or query. The proposed method is based on the fact that if a voter has submitted numerous elements which have been placed in high positions in the aggregated list, then this voter should be treated as an expert, compared to the voters whose suggestions appear in lower places or do not appear at all. The algorithm iteratively computes the distance of each input list with the aggregated list and modifies the weights of the voters until all weights converge. The effectiveness of the proposed method is experimentally demonstrated by aggregating input lists from six TREC conferences. 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 treating all voters equally. Nevertheless, several related works proved that the careful and effective assignment of different weights to each voter leads to enhanced performance. In this article, we introduce an unsupervised algorithm for learning the weights of the voters for a specific topic or query. The proposed method is based on the fact that if a voter has submitted numerous elements which have been placed in high positions in the aggregated list, then this voter should be treated as an expert, compared to the voters whose suggestions appear in lower places or do not appear at all. The algorithm iteratively computes the distance of each input list with the aggregated list and modifies the weights of the voters until all weights converge. The effectiveness of the proposed method is experimentally demonstrated by aggregating input lists from six TREC conferences.CCS CONCEPTS* Information systems → Rank aggregation; *Theory of computation → Unsupervised learning and clustering. |
| Author | Akritidis, Leonidas Bozanis, Panayiotis Fevgas, Athanasios |
| Author_xml | – sequence: 1 givenname: Leonidas surname: Akritidis fullname: Akritidis, Leonidas email: leoakr@e-ce.uth.gr organization: Department Electrical and Computer Engineering, University of Thessaly, Greece – sequence: 2 givenname: Athanasios surname: Fevgas fullname: Fevgas, Athanasios email: fevgas@e-ce.uth.gr organization: Department Electrical and Computer Engineering, University of Thessaly, Greece – sequence: 3 givenname: Panayiotis surname: Bozanis fullname: Bozanis, Panayiotis email: pbozanis@e-ce.uth.gr organization: Department Electrical and Computer Engineering, University of Thessaly, Greece |
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| Keywords | unsupervised rank aggregation weighted rank aggregation distance-based rank aggregation unsupervised learning |
| Language | English |
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| Snippet | Rank aggregation is a popular problem that combines different ranked lists from various sources (frequently called voters or judges), and generates a single... |
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| SubjectTerms | 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 |
| Title | An Iterative Distance-Based Model for Unsupervised Weighted Rank Aggregation |
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