LTRo: Learning to Route Queries in Clustered P2P IR

Rami S.  Alkhawaldeh; Deepak Padmanabhan; Joemon M. Jose; Fajie Yuan

doi:10.1007/978-3-319-56608-5_42

LTRo: Learning to Route Queries in Clustered P2P IR

Rami S. Alkhawaldeh, Deepak Padmanabhan, Joemon M. Jose, Fajie Yuan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

Query Routing is a critical step in P2P Information Retrieval. In thispaper, we consider learning to rank approaches for query routing in the clusteredP2P IR architecture. Our formulation, LTRo, scores resources based on the numberof relevant documents for each training query, and uses that information tobuild a model that would then rank promising peers for a new query. Our empiricalanalysis over a variety of P2P IR testbeds illustrate the superiority of ourmethod against the state-of-the-art methods for query routing.

Original language	English
Title of host publication	LTRo: Learning to Route Queries in Clustered P2P IR
Publisher	Springer
DOIs	https://doi.org/10.1007/978-3-319-56608-5_42
Publication status	Published - 08 Apr 2017
Event	ECIR 2017 - Aberdeen, Aberdeen, United Kingdom Duration: 08 Apr 2017 → 13 Apr 2017 http://www.ecir2017.org/

Publication series

Name	Lecture Notes in Computer Science
ISSN (Print)	0302-9743

Conference

Conference	ECIR 2017
Country/Territory	United Kingdom
City	Aberdeen
Period	08/04/2017 → 13/04/2017
Internet address	http://www.ecir2017.org/

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LTRo: Learning to Route Queries in Clustered P2P IR
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Cite this

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title = "LTRo: Learning to Route Queries in Clustered P2P IR",

abstract = "Query Routing is a critical step in P2P Information Retrieval. In thispaper, we consider learning to rank approaches for query routing in the clusteredP2P IR architecture. Our formulation, LTRo, scores resources based on the numberof relevant documents for each training query, and uses that information tobuild a model that would then rank promising peers for a new query. Our empiricalanalysis over a variety of P2P IR testbeds illustrate the superiority of ourmethod against the state-of-the-art methods for query routing.",

author = "Alkhawaldeh, {Rami S.} and Deepak Padmanabhan and Jose, {Joemon M.} and Fajie Yuan",

year = "2017",

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series = "Lecture Notes in Computer Science",

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AU - Alkhawaldeh, Rami S.

AU - Padmanabhan, Deepak

AU - Jose, Joemon M.

AU - Yuan, Fajie

PY - 2017/4/8

Y1 - 2017/4/8

N2 - Query Routing is a critical step in P2P Information Retrieval. In thispaper, we consider learning to rank approaches for query routing in the clusteredP2P IR architecture. Our formulation, LTRo, scores resources based on the numberof relevant documents for each training query, and uses that information tobuild a model that would then rank promising peers for a new query. Our empiricalanalysis over a variety of P2P IR testbeds illustrate the superiority of ourmethod against the state-of-the-art methods for query routing.

AB - Query Routing is a critical step in P2P Information Retrieval. In thispaper, we consider learning to rank approaches for query routing in the clusteredP2P IR architecture. Our formulation, LTRo, scores resources based on the numberof relevant documents for each training query, and uses that information tobuild a model that would then rank promising peers for a new query. Our empiricalanalysis over a variety of P2P IR testbeds illustrate the superiority of ourmethod against the state-of-the-art methods for query routing.

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DO - 10.1007/978-3-319-56608-5_42

M3 - Conference contribution

T3 - Lecture Notes in Computer Science

BT - LTRo: Learning to Route Queries in Clustered P2P IR

PB - Springer

T2 - ECIR 2017

Y2 - 8 April 2017 through 13 April 2017

ER -

LTRo: Learning to Route Queries in Clustered P2P IR

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