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http://dx.doi.org/10.9708/jksci.2015.20.3.047

Predicting Interesting Web Pages by SVM and Logit-regression  

Jeon, Dohong (Dept. of Computer Science, Catholic Kwandong University)
Kim, Hyoungrae (Korea Employment Information Service)
Publication Information
Journal of the Korea Society of Computer and Information / v.20, no.3, 2015 , pp. 47-56 More about this Journal
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 white box learning methods (fixed effect logit regression and support vector machine) to test empirically. The result indicated that (1) fixed effect logit regression, fixed effect SVMs with both polynomial and radial basis kernels showed higher performance than the linear kernel model, (2) a personalization is a critical issue 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, the ratio 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.
Keywords
machine learning; Implicit indicator; Web pages; Interest;
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Times Cited By KSCI : 2  (Citation Analysis)
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