CCRank: Parallel Learning to Rank with Cooperative Coevolution
Abstract
We propose CCRank, the first parallel algorithm for learning to rank, targeting simultaneous improvement in learning accuracy and efficiency. 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 subproblems, which can substantially boost learning efficiency. With CCRank, we investigate parallel CC in the context of learning to rank. Extensive experiments on benchmarks in comparison with the state-of-the-art algorithms show that CCRank gains in both accuracy and efficiency.
Cite
Text
Wang et al. "CCRank: Parallel Learning to Rank with Cooperative Coevolution." AAAI Conference on Artificial Intelligence, 2011. doi:10.1609/AAAI.V25I1.8078Markdown
[Wang et al. "CCRank: Parallel Learning to Rank with Cooperative Coevolution." AAAI Conference on Artificial Intelligence, 2011.](https://mlanthology.org/aaai/2011/wang2011aaai-ccrank/) doi:10.1609/AAAI.V25I1.8078BibTeX
@inproceedings{wang2011aaai-ccrank,
title = {{CCRank: Parallel Learning to Rank with Cooperative Coevolution}},
author = {Wang, Shuaiqiang and Gao, Byron J. and Wang, Ke and Lauw, Hady Wirawan},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2011},
pages = {1249-1254},
doi = {10.1609/AAAI.V25I1.8078},
url = {https://mlanthology.org/aaai/2011/wang2011aaai-ccrank/}
}