Name
Abstract | ||
|---|---|---|
This paper proposes an automated web site evaluation approach using machine learning to cope with ranking problems. Evaluating web sites is a significant task for web service because evaluated web sites provide useful information for users to estimate sites’ validation and popularity. Although many practical approaches have been taken to present a measuring stick for web sites, their evaluation functions are set up manually. Thus, we develop a method to obtain evaluation function using Ranking SVM and automatically rank web sites with the learned classifier. Also we conducted experiments and confirmed the effectiveness of our approach and its potential in performing high quality web site evaluation. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/WI-IAT.2009.224 | Web Intelligence/IAT Workshops |
Keywords | Field | DocType |
Ranking SVM,Web Site Evaluation | Data mining,Ranking SVM,Web page,Computer science,Artificial intelligence,The Internet,Information retrieval,Ranking,Support vector machine,Usability,Evaluation function,Web service,Machine learning | Conference |
Volume | Citations | PageRank |
3 | 1 | 0.36 |
References | Authors | |
3 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peng Li | 1 | 91 | 9.30 |
| Seiji Yamada | 2 | 373 | 60.21 |