Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
7-2011
Abstract
Learning to rank represents a category of effective ranking methods for information retrieval. While the primary concern of existing research has been accuracy, learning efficiency is becoming an important issue due to the unprecedented availability of large-scale training data and the need for continuous update of ranking functions. In this paper, we investigate parallel learning to rank, targeting simultaneous improvement in accuracy and efficiency.
Keywords
learning to rank, mapreduce, parallel algorithms, information retrieval, cooperative coevolution
Discipline
Databases and Information Systems
Publication
SIGIR '11: Proceeding of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval: Beijing, July 24-28
First Page
1083
Last Page
1084
ISBN
9781450307574
Identifier
10.1145/2009916.2010060
Publisher
ACM
City or Country
New York
Citation
WANG, Shuaiqiang; GAO, Byron J.; WANG, Ke; and LAUW, Hady W..
Parallel Learning to Rank for Information Retrieval. (2011). SIGIR '11: Proceeding of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval: Beijing, July 24-28. 1083-1084.
Available at: https://ink.library.smu.edu.sg/sis_research/1517
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/10.1145/2009916.2010060