Plackett-Luce Regression Mixture model for heterogeneous rankings

Learning to rank is an important problem in many scenarios, such as information retrieval, natural language processing, recommender systems, etc. The objective is to learn a function that ranks a number of instances based on their features. In the vast majority of the learning to rank literature, th...

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Main Authors: TKACHENKO, Maksim, LAUW, Hady W.
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Language:English
Published: Institutional Knowledge at Singapore Management University 2016
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Online Access:https://ink.library.smu.edu.sg/sis_research/3354
https://ink.library.smu.edu.sg/context/sis_research/article/4356/viewcontent/Plackett_luceRegression.pdf
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spelling sg-smu-ink.sis_research-43562020-03-30T01:50:25Z Plackett-Luce Regression Mixture model for heterogeneous rankings TKACHENKO, Maksim LAUW, Hady W. Learning to rank is an important problem in many scenarios, such as information retrieval, natural language processing, recommender systems, etc. The objective is to learn a function that ranks a number of instances based on their features. In the vast majority of the learning to rank literature, there is an implicit assumption that the population of ranking instances are homogeneous, and thus can be modeled by a single central ranking function. In this work, we are concerned with learning to rank for a heterogeneous population, which may consist of a number of sub-populations, each of which may rank objects dierently. Because these sub-populations are not known in advance, and are eectively latent, the problem turns into simultaneously learning both a set of ranking functions, as well as the latent assignment of instances to functions. To address this problem in a joint manner, we develop a probabilistic graphical model called Plackett-Luce Regression Mixture or PLRM model, and describe its inference via Expectation-Maximization algorithm. Comprehensive experiments on publicly-available real-life datasets showcase the eectiveness of PLRM, as opposed to a pipelined approach of clustering followed by learning to rank, as well as approaches that assume a single ranking function for a heterogeneous population 2016-10-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/3354 info:doi/10.1145/2983323.2983763 https://ink.library.smu.edu.sg/context/sis_research/article/4356/viewcontent/Plackett_luceRegression.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Mixture model Graphical model Plackett-Luce Heterogeneous Ranking Learning to rank Databases and Information Systems Numerical Analysis and Scientific Computing
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Mixture model
Graphical model
Plackett-Luce
Heterogeneous Ranking
Learning to rank
Databases and Information Systems
Numerical Analysis and Scientific Computing
spellingShingle Mixture model
Graphical model
Plackett-Luce
Heterogeneous Ranking
Learning to rank
Databases and Information Systems
Numerical Analysis and Scientific Computing
TKACHENKO, Maksim
LAUW, Hady W.
Plackett-Luce Regression Mixture model for heterogeneous rankings
description Learning to rank is an important problem in many scenarios, such as information retrieval, natural language processing, recommender systems, etc. The objective is to learn a function that ranks a number of instances based on their features. In the vast majority of the learning to rank literature, there is an implicit assumption that the population of ranking instances are homogeneous, and thus can be modeled by a single central ranking function. In this work, we are concerned with learning to rank for a heterogeneous population, which may consist of a number of sub-populations, each of which may rank objects dierently. Because these sub-populations are not known in advance, and are eectively latent, the problem turns into simultaneously learning both a set of ranking functions, as well as the latent assignment of instances to functions. To address this problem in a joint manner, we develop a probabilistic graphical model called Plackett-Luce Regression Mixture or PLRM model, and describe its inference via Expectation-Maximization algorithm. Comprehensive experiments on publicly-available real-life datasets showcase the eectiveness of PLRM, as opposed to a pipelined approach of clustering followed by learning to rank, as well as approaches that assume a single ranking function for a heterogeneous population
format text
author TKACHENKO, Maksim
LAUW, Hady W.
author_facet TKACHENKO, Maksim
LAUW, Hady W.
author_sort TKACHENKO, Maksim
title Plackett-Luce Regression Mixture model for heterogeneous rankings
title_short Plackett-Luce Regression Mixture model for heterogeneous rankings
title_full Plackett-Luce Regression Mixture model for heterogeneous rankings
title_fullStr Plackett-Luce Regression Mixture model for heterogeneous rankings
title_full_unstemmed Plackett-Luce Regression Mixture model for heterogeneous rankings
title_sort plackett-luce regression mixture model for heterogeneous rankings
publisher Institutional Knowledge at Singapore Management University
publishDate 2016
url https://ink.library.smu.edu.sg/sis_research/3354
https://ink.library.smu.edu.sg/context/sis_research/article/4356/viewcontent/Plackett_luceRegression.pdf
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