Publication Type

Conference Proceeding Article

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

Research Areas

Data Management and Analytics

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

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

https://doi.org/10.1145/2009916.2010060

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