Conference Proceeding Article
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.
learning to rank, mapreduce, parallel algorithms, information retrieval, cooperative coevolution
Databases and Information Systems
Data Management and Analytics
SIGIR '11: Proceeding of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval: Beijing, July 24-28
City or Country
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. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/1517
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