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Predicting Interesting Web Pages by SVM and Logit-regression

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2015, 20(3), pp.47-56
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science

Jun,Do-Hong 1 김형래 2

1가톨릭관동대학교
2한국고용정보원

Accredited

ABSTRACT

Automated detection of interesting web pages could be used in many different application domains. Determining a user’s interesting web pages can be performed implicitly by observing the user’s behavior. The task of distinguishing interesting web pages belongs to a classification problem, and we choose whitebox learning methods (fixed effect logit regression and support vector machine) to test empirically. Theresult indicated that (1) fixed effect logit regression, fixed effect SVMs with both polynomial and radialbasis kernels showed higher performance than the linear kernel model, (2) a personalization is a criticalissue for improving the performance of a model, (3) when asking a user explicit grading of web pages, the scale could be as simple as yes/no answer, (4) every second the duration in a web page increases, theratio of the probability to be interesting increased 1.004 times, but the number of scrollbar clicks (p=0.56)and the number of mouse clicks (p=0.36) did not have statistically significant relations with the interest.

Citation status

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