Publication Type
Journal Article
Version
acceptedVersion
Publication Date
12-2015
Abstract
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.
Keywords
Cooperative coevolution, learning to rank, information retrieval, genetic programming, immune programming
Discipline
Computer Sciences | Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
IEEE Transactions on Knowledge and Data Engineering
Volume
27
Issue
12
First Page
3152
Last Page
3165
ISSN
1041-4347
Identifier
10.1109/TKDE.2015.2453952
Publisher
IEEE
Citation
WANG, Shuaiqiang; WU, Yun; GAO, Byron J.; WANG, Ke; LAUW, Hady W.; and MA, Jun.
A Cooperative Coevolution Framework for Parallel Learning to Rank. (2015). IEEE Transactions on Knowledge and Data Engineering. 27, (12), 3152-3165.
Available at: https://ink.library.smu.edu.sg/sis_research/2889
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/TKDE.2015.2453952