A Cooperative Coevolution Framework for Parallel Learning to Rank

A Cooperative Coevolution Framework for Parallel Learning to Rank

We propose CCRank, the first parallel framework for learning to rank based on evolutionary algorithms (EA), aiming to significantly improve learning efficiency while maintaining accuracy. CCRank is based on cooperative coevolution (CC), a divide-and-conquer framework that has demonstrated high promi...

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Bibliographic Details
Main Authors: WANG, Shuaiqiang, WU, Yun, GAO, Byron J., WANG, Ke, LAUW, Hady W., MA, Jun
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2015
Subjects:
Cooperative coevolution
learning to rank
information retrieval
genetic programming
immune programming
Computer Sciences
Databases and Information Systems
Online Access:https://ink.library.smu.edu.sg/sis_research/2889
https://ink.library.smu.edu.sg/context/sis_research/article/3889/viewcontent/Lauw_2015_CCRank.pdf
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Institution: Singapore Management University
Language: English
Description
Summary:We propose CCRank, the first parallel framework for learning to rank based on evolutionary algorithms (EA), aiming to significantly improve learning efficiency while maintaining accuracy. CCRank is based on cooperative coevolution (CC), a divide-and-conquer framework that has demonstrated high promise in function optimization for problems with large search space and complex structures. Moreover, CC naturally allows parallelization of sub-solutions to the decomposed sub-problems, which can substantially boost learning efficiency. With CCRank, we investigate parallel CC in the context of learning to rank. We implement CCRank with three EA-based learning to rank algorithms for demonstration. Extensive experiments on benchmark datasets in comparison with the state-of-the-art algorithms show the performance gains of CCRank in efficiency and accuracy.

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