Article

Mobile content enrichment

Authors:

Barry SmythAuthors Info & Claims

IUI '07: Proceedings of the 12th international conference on Intelligent user interfaces

Pages 112 - 121

https://doi.org/10.1145/1216295.1216320

Published: 28 January 2007 Publication History

Abstract

Delivering an effective mobile search service is challenging for many reasons. Certainly small-screen mobile handsets with limited text input capabilities do not make ideal search devices. In addition, the brevity of Mobile Internet content hampers effective indexing and limits retrieval opportunities. In this paper we focus on this indexing issue and describe an approach that leverages Web search engines as a source of content enrichment. We present an evaluation using a mobile news service that demonstrated significant improvements in search performance compared to a standard benchmark system.

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Cited By

Chandra GDwivedi S(2024)Query Expansion Using Proposed Location-Based Algorithm for Hindi–English CLIR: Analyzing Three Test CollectionsInternational Journal of Pattern Recognition and Artificial Intelligence10.1142/S021800142459001838:05Online publication date: 11-May-2024
https://doi.org/10.1142/S0218001424590018
Khennak IEzzouar B(2018)Proximity-Based Good Turing Discounting and Kernel Functions for Pseudo-Relevance FeedbackInformation Retrieval and Management10.4018/978-1-5225-5191-1.ch100(2244-2266)Online publication date: 2018
https://doi.org/10.4018/978-1-5225-5191-1.ch100
Khennak IDrias H(2018)Strength Pareto fitness assignment for pseudo-relevance feedbackFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-016-5560-012:1(163-176)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1007/s11704-016-5560-0
Show More Cited By

Index Terms

Mobile content enrichment
1. Information systems
  1. Information retrieval
    1. Document representation
    2. Search engine architectures and scalability
      1. Search engine indexing

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cover image ACM Conferences

IUI '07: Proceedings of the 12th international conference on Intelligent user interfaces

January 2007

388 pages

ISBN:1595934812

DOI:10.1145/1216295

General Chairs:
David Chin
University of Hawaii, USA
,
Michelle Zhou
IBM Watson Research Center, USA
,
Program Chairs:
Tessa Lau
Almaden Research, USA
,
Angel Puerta
RedWhale Software, USA

Copyright © 2007 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 28 January 2007

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IUI07: 12th International Conference on Intelligent User Interfaces

January 28 - 31, 2007

Hawaii, Honolulu, USA

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Overall Acceptance Rate 746 of 2,811 submissions, 27%

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Cited By

Chandra GDwivedi S(2024)Query Expansion Using Proposed Location-Based Algorithm for Hindi–English CLIR: Analyzing Three Test CollectionsInternational Journal of Pattern Recognition and Artificial Intelligence10.1142/S021800142459001838:05Online publication date: 11-May-2024
https://doi.org/10.1142/S0218001424590018
Khennak IEzzouar B(2018)Proximity-Based Good Turing Discounting and Kernel Functions for Pseudo-Relevance FeedbackInformation Retrieval and Management10.4018/978-1-5225-5191-1.ch100(2244-2266)Online publication date: 2018
https://doi.org/10.4018/978-1-5225-5191-1.ch100
Khennak IDrias H(2018)Strength Pareto fitness assignment for pseudo-relevance feedbackFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-016-5560-012:1(163-176)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1007/s11704-016-5560-0
Drias HKhennak I(2017)Proximity-Based Good Turing Discounting and Kernel Functions for Pseudo-Relevance FeedbackInternational Journal of Information Retrieval Research10.4018/IJIRR.20170701017:3(1-21)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.4018/IJIRR.2017070101
Carpineto CRomano G(2012)A Survey of Automatic Query Expansion in Information RetrievalACM Computing Surveys10.1145/2071389.207139044:1(1-50)Online publication date: 1-Jan-2012
https://dl.acm.org/doi/10.1145/2071389.2071390
Chua AGoh DLee C(2012)Mobile content contribution and retrievalInformation Processing and Management: an International Journal10.1016/j.ipm.2011.04.00248:1(13-22)Online publication date: 1-Jan-2012
https://dl.acm.org/doi/10.1016/j.ipm.2011.04.002
Paek TLee BThiesson BOppermann REisenhauer MJarke MWulf V(2009)Designing phrase builderProceedings of the 11th International Conference on Human-Computer Interaction with Mobile Devices and Services10.1145/1613858.1613868(1-10)Online publication date: 15-Sep-2009
https://dl.acm.org/doi/10.1145/1613858.1613868
Carpineto CMizzaro SRomano GSnidero M(2009)Mobile information retrieval with search results clustering: Prototypes and evaluationsJournal of the American Society for Information Science and Technology10.1002/asi.2103660:5(877-895)Online publication date: 10-Feb-2009
https://doi.org/10.1002/asi.21036
Jeon HKim TChoi J(2008)Mobile Semantic Search Using Personal Preference FilteringProceedings of the 2008 Fourth International Conference on Networked Computing and Advanced Information Management - Volume 0210.1109/NCM.2008.175(531-534)Online publication date: 2-Sep-2008
https://dl.acm.org/doi/10.1109/NCM.2008.175
Church KSmyth B(2007)Improving mobile search using content enrichmentArtificial Intelligence Review10.1007/s10462-008-9073-628:1(87-102)Online publication date: 1-Jun-2007
https://dl.acm.org/doi/10.1007/s10462-008-9073-6

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