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Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model [article]

Jiaqi Ma, Xinyang Yi, Weijing Tang, Zhe Zhao, Lichan Hong, Ed H. Chi, Qiaozhu Mei
2021 arXiv   pre-print
We investigate the Plackett-Luce (PL) model based listwise learning-to-rank (LTR) on data with partitioned preference, where a set of items are sliced into ordered and disjoint partitions, but the ranking  ...  Given N items with M partitions, calculating the likelihood of data with partitioned preference under the PL model has a time complexity of O(N+S!)  ...  Acknowledgements The authors would like to thank Ao Liu, Tyler Lu, Lirong Xia, and Zhibing Zhao for helpful discussions.  ... 
arXiv:2006.05067v3 fatcat:ibyzs4u6era3rpsbckqi42dyla

Learning Plackett-Luce Mixtures from Partial Preferences

Ao Liu, Zhibing Zhao, Chao Liao, Pinyan Lu, Lirong Xia
2019 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
We propose an EM-based framework for learning Plackett-Luce model and its mixtures from partial orders.  ...  The core of our framework is the efficient sampling of linear extensions of partial orders under Plackett-Luce model.  ...  Acknowledgments We thank all anonymous reviewers for helpful comments and suggestions. This work is supported by NSF #1453542 and ONR #N00014-17-1-2621.  ... 
doi:10.1609/aaai.v33i01.33014328 fatcat:nluhabbdufhu3kb7ie5rledzmq

A flexible generative model for preference aggregation

Maksims N. Volkovs, Richard S. Zemel
2012 Proceedings of the 21st international conference on World Wide Web - WWW '12  
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.  ...  a consensus ranking.  ...  Under partial rankings the partition function can no longer be computed exactly, so these authors introduced a new sampling approach to estimate it.  ... 
doi:10.1145/2187836.2187902 dblp:conf/www/VolkovsZ12 fatcat:rzi4ql2ernbrjo3u6ajrpx5ffu

Supervised clustering of label ranking data using label preference information

Mihajlo Grbovic, Nemanja Djuric, Shengbo Guo, Slobodan Vucetic
2013 Machine Learning  
For example, in target marketing we might want to come up with K different offers or marketing strategies for our target audience.  ...  Our modification starts with K random label rankings and iteratively splits the feature space to minimize the ranking loss, followed by re-calculation of the K rankings based on cluster assignments.  ...  Acknowledgements We are grateful to Marina Meilȃ for providing software for the EBMS algorithm, and to the anonymous reviewers for constructive feedback and insightful suggestions which greatly improved  ... 
doi:10.1007/s10994-013-5374-3 fatcat:jiadjfzxufhtjkeasypz3iduzm

Preference-based Online Learning with Dueling Bandits: A Survey [article]

Viktor Bengs, Robert Busa-Fekete, Adil El Mesaoudi-Paul, Eyke Hüllermeier
2021 arXiv   pre-print
This observation has motivated the study of variants of the multi-armed bandit problem, in which more general representations are used both for the type of feedback to learn from and the target of prediction  ...  The aim of this paper is to provide a survey of the state of the art in this field, referred to as preference-based multi-armed bandits or dueling bandits.  ...  We would also like to thank two anonymous referees for their valuable comments and suggestions, which helped to significantly improve this survey.  ... 
arXiv:1807.11398v2 fatcat:jsu6gap5pbgbtm735fgf4aqwmu

Collaboratively Learning Preferences from Ordinal Data [article]

Sewoong Oh, Kiran K. Thekumparampil, Jiaming Xu
2015 arXiv   pre-print
In order to predict the preferences, we want to learn the underlying model from noisy observations of the low-rank matrix, collected as revealed preferences in various forms of ordinal data.  ...  A popular discrete choice model of multinomial logit model captures the structure of the hidden preferences with a low-rank matrix.  ...  model [15] or the Plackett-Luce model [14] , which are special cases of the MNL model studied in this paper.  ... 
arXiv:1506.07947v1 fatcat:kwina5etunds5b6vjckqp4o6z4

A Bayesian Choice Model for Eliminating Feedback Loops [article]

Gökhan Çapan, Ilker Gündoğdu, Ali Caner Türkmen, Çağrı Sofuoğlu, Ali Taylan Cemgil
2019 arXiv   pre-print
We propose a Bayesian choice model built on Luce axioms that explicitly accounts for users' limited exposure to alternatives.  ...  Our model is fair---it does not impose negative bias towards unpresented alternatives, and practical---preference estimates are accurately inferred upon observing a small number of interactions.  ...  Learning to Present Cast as the model assumption of an interactive system, the Dirichlet-Luce model provides fair and efficient preference estimates.  ... 
arXiv:1908.05640v2 fatcat:tptkhmde5vhftbtiy7u7by6me4

Supervised Clustering of Label Ranking Data [chapter]

Mihajlo Grbovic, Nemanja Djuric, Slobodan Vucetic
2012 Proceedings of the 2012 SIAM International Conference on Data Mining  
It is based on the Plackett-Luce (PL) probabilistic ranking model.  ...  The results showed that it is highly competitive to the state of the art label ranking algorithms, and that it is particularly accurate on data with partial rankings.  ...  Acknowledgments We are grateful to Marina Meila for providing the codes for the EBMS algorithm.  ... 
doi:10.1137/1.9781611972825.9 dblp:conf/sdm/GrbovicDV12 fatcat:6mos4tpxzfbz5ohzrftggaqxgq

From PAC to Instance-Optimal Sample Complexity in the Plackett-Luce Model [article]

Aadirupa Saha, Aditya Gopalan
2020 arXiv   pre-print
We consider PAC-learning a good item from k-subsetwise feedback information sampled from a Plackett-Luce probability model, with instance-dependent sample complexity performance.  ...  We next address the winner-finding problem in Plackett-Luce models in the fixed-budget setting with instance dependent upper and lower bounds on the misidentification probability, of Ω((-2 Δ̃Q) ) for a  ...  In COLT-23th Conference on Learning Theory-2010, pages 13-p, 2010.  ... 
arXiv:1903.00558v2 fatcat:kdzdnvf3hjbwponrfou526kgsa

Random Utility Theory for Social Choice [article]

Hossein Azari Soufiani, David C. Parkes, Lirong Xia
2012 arXiv   pre-print
A special case that has received significant attention is the Plackett-Luce model, for which fast inference methods for maximum likelihood estimators are available.  ...  Results on both real-world and simulated data provide support for the scalability of the approach and capability for model selection among general random utility models including Plackett-Luce.  ...  Lirong Xia is supported by NSF under Grant #1136996 to the Computing Research Association for the CIFellows Project.  ... 
arXiv:1211.2476v1 fatcat:f2hvt7tburfqbi6bdynhkbvfka

Composite Marginal Likelihood Methods for Random Utility Models [article]

Zhibing Zhao, Lirong Xia
2018 arXiv   pre-print
We propose a novel and flexible rank-breaking-then-composite-marginal-likelihood (RBCML) framework for learning random utility models (RUMs), which include the Plackett-Luce model.  ...  Experiments on synthetic data show that RBCML for Gaussian RUMs achieves better statistical efficiency and computational efficiency than the state-of-the-art algorithm and our RBCML for the Plackett-Luce  ...  Acknowledgments We thank all anonymous reviewers for helpful comments and suggestions. This work is supported by NSF #1453542 and ONR #N00014-17-1-2621.  ... 
arXiv:1806.01426v1 fatcat:3el5h3rbpfhjhdxkbxia46fd24

Learning From Ordered Sets and Applications in Collaborative Ranking [article]

Truyen Tran, Dinh Phung, Svetha Venkatesh
2014 arXiv   pre-print
It turns out, to model this data type properly, we need to investigate the general combinatorics problem of partitioning a set and ordering the subsets.  ...  For discovering hidden aspects in the data, we enrich the model with latent binary variables so that the posteriors can be efficiently evaluated.  ...  [17] and the matrix-factored Plackett-Luce model [19] (Plackett-Luce.MF ).  ... 
arXiv:1408.0043v1 fatcat:zz5akgoj2rcwjmhs2tlytj4rbu

Accelerated MM Algorithms for Ranking Scores Inference from Comparison Data [article]

Milan Vojnovic and Seyoung Yun and Kaifang Zhou
2020 arXiv   pre-print
This class of models includes the Bradley-Terry model of paired comparisons, the Rao-Kupper model of paired comparisons allowing for tie outcomes, the Luce choice model, and the Plackett-Luce ranking model  ...  For the maximum likelihood estimation, the convergence is shown to be linear with the rate crucially determined by the algebraic connectivity of the matrix of item pair co-occurrences in observed comparison  ...  Plackett-Luce ranking model.  ... 
arXiv:1901.00150v3 fatcat:5pcwzn7qkbftdoyqeumhz5vxhm

Learning Neural Ranking Models Online from Implicit User Feedback

Yiling Jia, Hongning Wang
2022 Proceedings of the ACM Web Conference 2022  
In this work, to unleash the power of representation learning in OL2R, we propose to directly learn a neural ranking model from users' implicit feedback (e.g., clicks) collected on the fly.  ...  Existing online learning to rank (OL2R) solutions are limited to linear models, which are incompetent to capture possible non-linear relations between queries and documents.  ...  ACKNOWLEDGMENTS This paper is based upon the work supported by the National Science Foundation under grant IIS-1553568 and IIS-2128019, and Google Faculty Research Award.  ... 
doi:10.1145/3485447.3512250 fatcat:ojp2xhpv7nhmdodg6zbfkdgyx4

A review on ranking problems in statistical learning [article]

Tino Werner
2020 arXiv   pre-print
Ranking problems, also known as preference learning problems, define a widely spread class of statistical learning problems with many applications, including fraud detection, document ranking, medicine  ...  As for a detailed and comprehensive overview of existing machine learning techniques to solve such ranking problems, we systemize existing techniques and recapitulate the corresponding optimization problems  ...  The Plackett-Luce model (Luce [1959] , Plackett [1975] ) performs a Bayes estimation.  ... 
arXiv:1909.02998v3 fatcat:lufafwehgvbzbekvabyc2oq72y
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