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 proceedingConference 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 languageEnglish
Title of host publicationLTRo: Learning to Route Queries in Clustered P2P IR
PublisherSpringer
DOIs
Publication statusPublished - 08 Apr 2017
EventECIR 2017 - Aberdeen, Aberdeen, United Kingdom
Duration: 08 Apr 201713 Apr 2017
http://www.ecir2017.org/

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743

Conference

ConferenceECIR 2017
CountryUnited Kingdom
CityAberdeen
Period08/04/201713/04/2017
Internet address

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  • Cite this

    Alkhawaldeh, R. S., Padmanabhan, D., Jose, J. M., & Yuan, F. (2017). LTRo: Learning to Route Queries in Clustered P2P IR. In LTRo: Learning to Route Queries in Clustered P2P IR (Lecture Notes in Computer Science). Springer. https://doi.org/10.1007/978-3-319-56608-5_42