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XML search personalization strategies using query expansion, reranking and a search engine modification

Published: 18 March 2013 Publication History

Abstract

As the amount of information increases every day and queries are most of the times short and ambiguous, search personalization techniques are becoming almost a must. These techniques retrieve relevant results closer to the user, covering in a better and easier way his/her information needs. This work is focused on improving the personalized retrieval process, trying to combine the advantages of all the approaches used for personalization: query reformulation, reranking of results and search engine modification. The proposed personalization techniques have been designed for XML retrieval, but they could easily be applied to non structured documents. The experimental results obtained from a user study using a parliamentary document collection support the validity of our approach.

References

[1]
S. Amer-Yahia, I. Fundulaki, and L. V. S. Lakshmanan. Personalizing XML search in PIMENTO. In Proceedings of the 23rd IEEE ICDE Conference, pp.906--915, 2007.
[2]
L. M. de Campos, J. M. Fernández-Luna, and J. F. Huete. Using context information in structured document retrieval: An approach using influence diagrams. Inf. Process. Manage., 40(5):829--847, 2004.
[3]
L. M. de Campos, J. M. Fernández-Luna, J. F. Huete, and A. E. Romero. Garnata: An information retrieval system for structured documents based on probabilistic graphical models. In Proceedings of the 11th IPMU Conference, pp.1024--1031, 2006.
[4]
C. Carpineto, and G. Romano. A survey of automatic query expansion in information retrieval. ACM Comput. Surv., 44(1), Article 1, 2012.
[5]
P. A. Chirita, C. S. Firan, and W. Nejdl. Personalized query expansion for the web. In Proceedings of the 30th SIGIR Conference, pp.7--14, 2007.
[6]
Z. Dou, R. Song, and J. R. Wen. A large-scale evaluation and analysis of personalized search strategies. In Proceedings of the 16th WWW Conference, pp.581--590, 2007.
[7]
T. Haveliwala. Topic-sensitive PageRank: a context-sensitive ranking algorithm for web search. IEEE Trans. Knowl. Data Eng., 15(4):784--796, 2003.
[8]
W. Hsu, M. L. Lee, and X. Wu. Path-augmented keyword search for XML documents. In Proceedings of the 16th IEEE ICTAI Conference, pp.526--530, 2004.
[9]
K. Jarvelin, and J. Kekalainen. Cumulative gain-based evaluation of IR techniques. ACM Trans. Inf. Syst., 20(4):422--446, 2002.
[10]
J. Kamps, J. Pehcevski, G. Kazai, M. Lalmas, and S. Robertson. INEX 2007 Evaluation Measures. In INEX 2007, LNCS 4862:24--33, 2008.
[11]
G. Kazai, and M. Lalmas. INEX 2005 Evaluation Measures. In INEX 2005, LNCS 3977:16--29, 2006.
[12]
M. Lalmas. XML Retrieval. Morgan & Claypool Publishers, 2009.
[13]
K. S. Lee, W. B. Croft, and J. Allan. A cluster-based resampling method for pseudo-relevance feedback. In Proceedings of the 31th SIGIR Conference, pp.235--242, 2008.
[14]
C. Macdonald, and I. Ounis. Expertise drift and query expansion in expert search. In Proceedings of the 16th ACM CIKM Conference, pp.341--350, 2007.
[15]
L. Meister, O. Kurland, and I. G. Kalmanovich. Two are better than one! re-ranking search results using an additional retrieved list. Technical report IE/IS-2009-01, Technion, 2009.
[16]
R. Schenkel, and M. Theobald. Feedback-driven structural query expansion for ranked retrieval of XML data. In EDBT 2006, LNCS 3896:331--348, 2006.
[17]
A. Sieg, B. Mobasher, and R. Burke. Web search personalization with ontological user profiles. In Proceedings of the 16th ACM CIKM Conference, pp.525--534, 2007.
[18]
K. Sugiyama, K. Hatano, and M. Yoshikawa. Adaptive web search based on user profile constructed without any effort from users. In Proceedings of the 13th WWW Conference, pp.675--684, 2004.
[19]
L. Tamine-Lechani, M. Boughanem, and M. Daoud. Evaluation of contextual information retrieval effectiveness: overview of issues and research. Know. Inf. Syst., 24:1--34, 2010.
[20]
L. Zighelnic, and O. Kurland. Query-Drift Prevention for Robust Query Expansion. In Proceedings of the 31th SIGIR Conference, pp.825--826, 2008.

Cited By

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  • (2023)Combining offline and on-the-fly disambiguation to perform semantic-aware XML queryingComputer Science and Information Systems10.2298/CSIS220228063T20:1(423-457)Online publication date: 2023
  • (2021)Almost Linear Semantic XML Keyword SearchProceedings of the 13th International Conference on Management of Digital EcoSystems10.1145/3444757.3485079(129-138)Online publication date: 1-Nov-2021
  • (2017)Profile-based recommendationJournal of Information Science10.1177/016555151665940243:5(665-682)Online publication date: 1-Oct-2017
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  1. XML search personalization strategies using query expansion, reranking and a search engine modification

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    cover image ACM Conferences
    SAC '13: Proceedings of the 28th Annual ACM Symposium on Applied Computing
    March 2013
    2124 pages
    ISBN:9781450316569
    DOI:10.1145/2480362
    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: 18 March 2013

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    Author Tags

    1. XML
    2. information retrieval
    3. personalization
    4. query expansion
    5. query-drift
    6. reranking

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    SAC '13: SAC '13
    March 18 - 22, 2013
    Coimbra, Portugal

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    SAC '13 Paper Acceptance Rate 255 of 1,063 submissions, 24%;
    Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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    Cited By

    View all
    • (2023)Combining offline and on-the-fly disambiguation to perform semantic-aware XML queryingComputer Science and Information Systems10.2298/CSIS220228063T20:1(423-457)Online publication date: 2023
    • (2021)Almost Linear Semantic XML Keyword SearchProceedings of the 13th International Conference on Management of Digital EcoSystems10.1145/3444757.3485079(129-138)Online publication date: 1-Nov-2021
    • (2017)Profile-based recommendationJournal of Information Science10.1177/016555151665940243:5(665-682)Online publication date: 1-Oct-2017
    • (2015)An automatic methodology to evaluate personalized information retrieval systemsUser Modeling and User-Adapted Interaction10.1007/s11257-014-9148-925:1(1-37)Online publication date: 1-Mar-2015
    • (2014)Using Personalization to Improve XML RetrievalIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2013.7526:5(1280-1292)Online publication date: 1-May-2014

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