Chen Chen
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.
- 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. Google Scholar
Digital Library
- Shen, X., Tan, B., and Zhai, C. X. 2005. Implicit user modeling for personalized search. In Proc. of CIKM '05, 824--831. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- Teevan, J., Dumais, S. T., and Horvitz, E. 2010. Potential for personalization. To appear in ACM Transaction on Computer Human Interaction. Google ScholarDigital Library
- libSVM. http:// www.csie.ntu.edu.tw/~cjlin/libsvm/Google Scholar
Index Terms
- Predicting query potential for personalization, classification or regression?
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