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2011
ACM

Parallel learning to rank for information retrieval

7 years 9 months ago
Parallel learning to rank for information retrieval
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. Categories and Subject Descriptors: I.2.6 [Artificial Intelligence]: Learning; H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval General Terms: Algorithms, Performance.
Shuaiqiang Wang, Byron J. Gao, Ke Wang, Hady Wiraw
Added 17 Sep 2011
Updated 17 Sep 2011
Type Journal
Year 2011
Where SIGIR
Authors Shuaiqiang Wang, Byron J. Gao, Ke Wang, Hady Wirawan Lauw
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