Parallel Learning to Rank for Information Retrieval

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 cont...

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Bibliographic Details
Main Authors: WANG, Shuaiqiang, GAO, Byron J., WANG, Ke, LAUW, Hady W.
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2011
Subjects:
learning to rank
mapreduce
parallel algorithms
information retrieval
cooperative coevolution
Databases and Information Systems
Online Access:https://ink.library.smu.edu.sg/sis_research/1517
https://ink.library.smu.edu.sg/context/sis_research/article/2516/viewcontent/sigir11.pdf
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Institution: Singapore Management University
Language: English
Description
Summary: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.

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