Publication Type

Conference Proceeding Article

Version

Postprint

Publication Date

10-2016

Abstract

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

Keywords

Mixture model, Graphical model, Plackett-Luce, Heterogeneous Ranking, Learning to rank

Discipline

Software Engineering | Theory and Algorithms

Research Areas

Software and Cyber-Physical Systems

Publication

CIKM 2016: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management: Indianapolis, October 24-28, 2016

First Page

237

Last Page

246

ISBN

9781450340731

Identifier

10.1145/2983323.2983763

Publisher

ACM

City or Country

New York

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org/10.1145/2983323.2983763

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