Article

Personalized ranking: a contextual ranking approach

Authors:

Seung-won HwangAuthors Info & Claims

SAC '07: Proceedings of the 2007 ACM symposium on Applied computing

Pages 506 - 510

https://doi.org/10.1145/1244002.1244119

Published: 11 March 2007 Publication History

Abstract

As data of an unprecedented scale are becoming accessible on the Web, personalization, of narrowing down the retrieval to meet the user-specific information needs, is becoming more and more critical. For instance, in the context of text retrieval, in contrast to traditional web search engines retrieving the same results for all users, major commercial search engines are starting to support personalization, improving the search quality by adapting to the user-specific retrieval contexts, e.g., prior search history or other application contexts. This paper studies how to enable such personalization in the context of structured data retrieval. In particular, we adopt context-sensitive ranking model to formalize personalization as a cost-based optimization over context-sensitive rankings collected. With this formalism, personalization is essentially retrieving the context-sensitive ranking matching the specific user's retrieval context and generating a personalized ranking accordingly. In particular, we adopt a machine learning approach, to effectively and efficiently identify the ideal personalized ranked results for this specific user. Our empirical evaluations over real-life data validate both the effectiveness and efficiency of our framework.

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Cited By

Liu JLiu CBelkin N(2020)Personalization in text information retrievalJournal of the Association for Information Science and Technology10.1002/asi.2423471:3(349-369)Online publication date: 28-Jan-2020
https://dl.acm.org/doi/10.1002/asi.24234
Tamine LDaoud M(2018)Evaluation in Contextual Information RetrievalACM Computing Surveys10.1145/320494051:4(1-36)Online publication date: 25-Jul-2018
https://dl.acm.org/doi/10.1145/3204940
Bozzon ABrambilla MCorcoglioniti FVadacca S(2010)A Service-Based Architecture for Multi-domain Search on the WebService-Oriented Computing10.1007/978-3-642-17358-5_53(663-669)Online publication date: 2010
https://doi.org/10.1007/978-3-642-17358-5_53
Show More Cited By

Index Terms

Personalized ranking: a contextual ranking approach
1. Information systems
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Database theory
      1. Database query processing and optimization (theory)

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As data of an unprecedented scale are becoming accessible on the Web, personalization, of narrowing down the retrieval to meet the user-specific information needs, is becoming more and more critical. For instance, while web search engines traditionally ...

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Published In

cover image ACM Conferences

SAC '07: Proceedings of the 2007 ACM symposium on Applied computing

March 2007

1688 pages

ISBN:1595934804

DOI:10.1145/1244002

Conference Chairs:
Yookun Cho
Seoul National University, Seoul, Korea
,
Roger L. Wainwright
University of Tulsa, Tulsa, Oklahoma
,
Hisham M. Haddad
Kennesaw State University, Kennesaw, Georgia
,
Sung Y. Shin
South Dakota State University, Brookings, South Dakota
,
Program Chair:
Yong Wan Koo
The University of Suwon, Gyeongggi-do, Korea

Copyright © 2007 ACM.

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SIGAPP: ACM Special Interest Group on Applied Computing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 March 2007

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SAC07

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SAC07: The 2007 ACM Symposium on Applied Computing

March 11 - 15, 2007

Seoul, Korea

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

Liu JLiu CBelkin N(2020)Personalization in text information retrievalJournal of the Association for Information Science and Technology10.1002/asi.2423471:3(349-369)Online publication date: 28-Jan-2020
https://dl.acm.org/doi/10.1002/asi.24234
Tamine LDaoud M(2018)Evaluation in Contextual Information RetrievalACM Computing Surveys10.1145/320494051:4(1-36)Online publication date: 25-Jul-2018
https://dl.acm.org/doi/10.1145/3204940
Bozzon ABrambilla MCorcoglioniti FVadacca S(2010)A Service-Based Architecture for Multi-domain Search on the WebService-Oriented Computing10.1007/978-3-642-17358-5_53(663-669)Online publication date: 2010
https://doi.org/10.1007/978-3-642-17358-5_53
Arens R(2010)Learning SVM Ranking Functions from User Feedback Using Document Metadata and Active Learning in the Biomedical DomainPreference Learning10.1007/978-3-642-14125-6_17(363-383)Online publication date: 3-Sep-2010
https://doi.org/10.1007/978-3-642-14125-6_17
Hung CChi YChen T(2009)An attentive self-organizing neural model for text miningExpert Systems with Applications: An International Journal10.1016/j.eswa.2008.08.03736:3(7064-7071)Online publication date: 1-Apr-2009
https://dl.acm.org/doi/10.1016/j.eswa.2008.08.037
You GHwang S(2008)Search structures and algorithms for personalized rankingInformation Sciences: an International Journal10.1016/j.ins.2008.06.009178:20(3925-3942)Online publication date: 1-Oct-2008
https://dl.acm.org/doi/10.1016/j.ins.2008.06.009

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