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Predicting query potential for personalization, classification or regression?

Published: 19 July 2010 Publication History

Abstract

The goal of predicting query potential for personalization is to determine which queries can benefit from personalization. In this paper, we investigate which kind of strategy is better for this task: classification or regression. We quantify the potential benefits of personalizing search results using two implicit click-based measures: Click entropy and Potential@N. Meanwhile, queries are characterized by query features and history features. Then we build C-SVM classification model and epsilon-SVM regression model respectively according to these two measures. The experimental results show that the classification model is a better choice for predicting query potential for personalization.

References

[1]
Chirita, P. A., Nejdl, W., Paiu, R., and Kohlschutter, R,C. 2005. Using ODP metadata to personalize search. In Proc. of SIGIR'05, 178--185.
[2]
Shen, X., Tan, B., and Zhai, C. X. 2005. Implicit user modeling for personalized search. In Proc. of CIKM '05, 824--831.
[3]
Teevan, J., Dumais, S.T., and Horvitz, E. 2005. Personalizing search via automated analysis of interests and activities. In Proc. of SIGIR '05, 449--456.
[4]
Dou, Z., Song, R., and Wen, J.R. 2007. A large-scale evaluation and analysis of personalized search strategies. In Proc. of WWW '07, 581--590.
[5]
Teevan, J., Dumais, S. T., and Horvitz, E. 2008. To personalize or not to personalize: modeling queries with variation in user intent. In Proc. of SIGIR '08, 163--170.
[6]
Teevan, J., Dumais, S. T., and Horvitz, E. 2010. Potential for personalization. To appear in ACM Transaction on Computer Human Interaction.
[7]
libSVM. http:// www.csie.ntu.edu.tw/~cjlin/libsvm/

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    cover image ACM Conferences
    SIGIR '10: Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
    July 2010
    944 pages
    ISBN:9781450301534
    DOI:10.1145/1835449
    Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Publication History

    Published: 19 July 2010

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    Author Tags

    1. classification
    2. query potential for personalization
    3. regression

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    SIGIR '10 Paper Acceptance Rate 87 of 520 submissions, 17%;
    Overall Acceptance Rate 792 of 3,983 submissions, 20%

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