Extraction of Web Site Evaluation Criteria and Automatic Evaluation
Peng Li* and Seiji Yamada**
*Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, J2, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan
**National Institute of Informatics, SOKENDAI, 2-1-2 Hitotsubashi, Chiyoda, Tokyo 101-8430, Japan
This paper proposes an automated web site evaluation using machine learning to extract evaluation criteria from existing evaluation data. Web site evaluation is a significant task because evaluated web sites provide information useful to users in estimating sites validation and popularity. Although many practical approaches have been taken to present possible measuring sticks for web sites, their evaluation criteria are manually determined. We developed a method to obtain evaluation criteria automatically and rank web sites with the learned classifier. Evaluation criteria are discriminant functions learned from a set of ranking information and evaluation features collected automatically by web robots. Experiments confirmed the effectiveness of our approach and its potential in high-quality web site evaluation.
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