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.
Databases and Information Systems
Data Management and Analytics
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval (SIGIR'11)
WANG, Shuaiqiang; GAO, Byron J.; WANG, Ke; and LAUW, Hady Wirawan.
Parallel Learning to Rank for Information Retrieval. (2011). Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval (SIGIR'11). 1083-1084. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/1517