research-article

A flexible generative model for preference aggregation

Authors:

Maksims N. Volkovs,

Richard S. ZemelAuthors Info & Claims

WWW '12: Proceedings of the 21st international conference on World Wide Web

Pages 479 - 488

https://doi.org/10.1145/2187836.2187902

Published: 16 April 2012 Publication History

Abstract

Many areas of study, such as information retrieval, collaborative filtering, and social choice face the preference aggregation problem, in which multiple preferences over objects must be combined into a consensus ranking. Preferences over items can be expressed in a variety of forms, which makes the aggregation problem difficult. In this work we formulate a flexible probabilistic model over pairwise comparisons that can accommodate all these forms. Inference in the model is very fast, making it applicable to problems with hundreds of thousands of preferences. Experiments on benchmark datasets demonstrate superior performance to existing methods

References

[1]

J. A. Aslam and M. Montague. Models for metasearch. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 276--284, 2001.

Digital Library

[2]

R. Baeza-Yates and B. Ribeiro-Neto. Information Retrieval. Addison-Wesley, 1999.

Digital Library

[3]

R. Bradley and M. Terry. Rank analysis of incomplete block designs. I. The method of paired comparisons. Biometrika, 39:324--345, 1952.

[4]

C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender. Learning to rank using gradient descent. In Proceedings of the International Conference on Machine Learning, pages 89--96, 2005.

Digital Library

[5]

P. Dangauthier, R. Herbrich, T. Minka, and T. Graepel. TrueSkill through time: Revisiting the history of chess. In Proceedings of the Neural Information Processing Systems, 2007.

[6]

A. E. Elo. The rating of chess players: Past and present. Acro Publishing, 1978.

[7]

R. Fagin, R. Kumar, and D. Sivakumar. Efficient similarity search and classification via rank aggregation. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 301--312, 2003.

Digital Library

[8]

D. F. Gleich and L.-H. Lim. Rank aggregation via nuclear norm minimization. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 60--68, 2011.

Digital Library

[9]

J. Guiver and E. Snelson. Bayesian inference for Plackett-Luce ranking models. In Proceedings of the International Conference on Machine Learning, pages 377--384, 2009.

Digital Library

[10]

K. Jarvelin and J. Kekalainen. IR evaluation methods for retrieving highly relevant documents. In Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval, pages 41--48, 2000.

Digital Library

[11]

X. Jiang, L.-H. Lim, Y. Yao, and Y. Ye. Statistical ranking and combinatorial hodge theory. Mathematical Programming, 127:203--244, 2011.

Digital Library

[12]

A. Klementiev, D. Roth, and K. Small. Unsupervised rank aggregation with distance-based models. In Proceedings of the International Conference on Machine Learning, pages 472--479, 2008.

Digital Library

[13]

G. Lebanon and J. Lafferty. Cranking: Combining rankings using conditional probability models on permutations. In Proceedings of the International Conference on Machine Learning, pages 363--370, 2002.

Digital Library

[14]

T. Liu, J. Xu, W. Xiong, and H. Li. LETOR: Benchmark dataset for search on learning to rank for information retrieval. In ACM SIGIR Workshop on Learning to Rank for Information Retrieval, 2007.

[15]

Y.-T. liu, T.-Y. Liu, T. Qin, Z.-M. Ma, and H. Li. Supervised rank aggregation. In Proceedings of the International conference on World Wide Web, pages 481--489, 2007.

Digital Library

[16]

T. Lu and C. Boutilier. Learning Mallows models with pairwise preferences. In Proceedings of the International Conference on Machine Learning, 2011.

[17]

R. D. Luce. Individual choice behavior: A theoretical analysis. Wiley, 1959.

[18]

C. L. Mallows. Non-null ranking models. Biometrika, 44:114--130, 1957.

[19]

B. M. Marlin, R. S. Zemel, and S. T. Roweis. Unsupervised learning with non-ignorable missing data. In Proceedings of the International Conference on Artificial Intelligence and Statistics, pages 222--229, 2005.

[20]

D. Mase. A penalized maximum likelihood approach for the ranking of college football teams independent of victory margins. The American Statistician, 57:241--248, 2003.

[21]

M. Meila, K. Phadnis, A. Patterson, and J. Bilmes. Consensus ranking under the exponential model. In Proceedings of the Uncertainty in Artificial Intelligence Conference, 2007.

[22]

R. Plackett. The analysis of permutations. Applied Statistics, 24:193--302, 1975.

[23]

T. Quin, X. Geng, and T.-Y. Liu. A new probabilistic model for rank aggregation. In Proceedings of the Neural Information Processing Systems, pages 681--689, 2010.

[24]

R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. In Proceedings of the Neural Information Processing Systems, volume 20, 2008.

[25]

L. L. Thurstone. The method of paired comparisons for social values. Journal of Abnormal and Social Psychology, 21:384--400, 1927.

Cited By

Zhang DLauw HDemartini GZuccon GCulpepper JHuang ZTong H(2021)Topic Modeling for Multi-Aspect Listwise ComparisonsProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482398(2507-2516)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482398
Lim APope S(2021)What drives companies to do good? A “universal” ordering of corporate social responsibility motivationsCorporate Social Responsibility and Environmental Management10.1002/csr.219929:1(233-255)Online publication date: 31-Aug-2021
https://doi.org/10.1002/csr.2199
Agarwal AAgarwal SKhanna SPatil PDaumé HSingh A(2020)Rank aggregation from pairwise comparisons in the presence of adversarial corruptionsProceedings of the 37th International Conference on Machine Learning10.5555/3524938.3524947(85-95)Online publication date: 13-Jul-2020
https://dl.acm.org/doi/10.5555/3524938.3524947
Show More Cited By

Index Terms

A flexible generative model for preference aggregation
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

Recommendations

New learning methods for supervised and unsupervised preference aggregation

In this paper we present a general treatment of the preference aggregation problem, in which multiple preferences over objects must be combined into a single consensus ranking. We consider two instances of this problem: unsupervised aggregation where no ...
User preference representation based on psychometric models
ADC '11: Proceedings of the Twenty-Second Australasian Database Conference - Volume 115

Neighbourhood-based collaborative filtering is one of the most popular recommendation techniques, and has been applied successfully in various fields. User ratings are often used by neighbourhood-based collaborative filtering to compute the similarity ...
Tutorial on preference handling
RecSys '13: Proceedings of the 7th ACM conference on Recommender systems

This tutorial, addressed to researchers and practitioners in the area of recommendation systems, reviews the foundations of decision and utility theory, focusing on methods for representing, learning and reasoning with preferences. Recent advances on ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

WWW '12: Proceedings of the 21st international conference on World Wide Web

April 2012

1078 pages

ISBN:9781450312295

DOI:10.1145/2187836

General Chairs:
Alain Mille
Université de Lyon, France
,
Fabien Gandon
INRIA, France
,
Jacques Misselis
HP, France
,
Program Chairs:
Michael Rabinovich
Case Western Reserve University, USA
,
Steffen Staab
University of Koblenz-Landau, Germany

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Univ. de Lyon: Universite de Lyon

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 April 2012

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

WWW 2012

Sponsor:

Univ. de Lyon

WWW 2012: 21st World Wide Web Conference 2012

April 16 - 20, 2012

Lyon, France

Acceptance Rates

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

25
Total Citations
View Citations
361
Total Downloads

Downloads (Last 12 months)12
Downloads (Last 6 weeks)0

Reflects downloads up to 24 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Zhang DLauw HDemartini GZuccon GCulpepper JHuang ZTong H(2021)Topic Modeling for Multi-Aspect Listwise ComparisonsProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482398(2507-2516)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482398
Lim APope S(2021)What drives companies to do good? A “universal” ordering of corporate social responsibility motivationsCorporate Social Responsibility and Environmental Management10.1002/csr.219929:1(233-255)Online publication date: 31-Aug-2021
https://doi.org/10.1002/csr.2199
Agarwal AAgarwal SKhanna SPatil PDaumé HSingh A(2020)Rank aggregation from pairwise comparisons in the presence of adversarial corruptionsProceedings of the 37th International Conference on Machine Learning10.5555/3524938.3524947(85-95)Online publication date: 13-Jul-2020
https://dl.acm.org/doi/10.5555/3524938.3524947
Chandra Sekhar Reddy LMurali DRajeshwar J(2019)A Review on Mobile App Ranking Review and Rating Fraud Detection in Big DataInnovations in Computer Science and Engineering10.1007/978-981-13-7082-3_63(551-556)Online publication date: 19-Jun-2019
https://doi.org/10.1007/978-981-13-7082-3_63
Lin YChakhar SYang R(2018)Sequential Rank Aggregation Method2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC.2018.00541(3194-3200)Online publication date: Oct-2018
https://doi.org/10.1109/SMC.2018.00541
Tavanaei AGottumukkalay RMaida ARaghavan V(2018)Unsupervised Learning to Rank Aggregation using Parameterized Function Optimization2018 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2018.8489160(1-8)Online publication date: Jul-2018
https://doi.org/10.1109/IJCNN.2018.8489160
Pan YHan BTsang I(2018)Stagewise learning for noisy k-ary preferencesMachine Language10.1007/s10994-018-5716-2107:8-10(1333-1361)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1007/s10994-018-5716-2
Negahban SOh SShah D(2017)Rank CentralityOperations Research10.1287/opre.2016.153465:1(266-287)Online publication date: 1-Feb-2017
https://dl.acm.org/doi/10.1287/opre.2016.1534
Barragan SRueda CFernandez M(2017)Circular Order Aggregation and Its Application to Cell-Cycle Genes ExpressionsIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2016.256546914:4(819-829)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1109/TCBB.2016.2565469
Iwanari TYoshinaga NKaj NNishina TToyoda MKitsuregawa M(2016)Ordering concepts based on common attribute intensityProceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence10.5555/3061053.3061143(3747-3753)Online publication date: 9-Jul-2016
https://dl.acm.org/doi/10.5555/3061053.3061143
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents