Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Smartback: supporting users in back navigation
Proceedings of the 13th international conference on World Wide Web
Learning to cluster web search results
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
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Nowadays, Web activities have become daily practice for people. It is therefore essential to organize and present this continuously increasing Web information in a more usable manner. In this paper, we developed a novel approach to reorganize personal Web information as a topic-oriented interface. In our approach, we proposed to utilize anchor, title and URL information to represent content information for the browsed Web pages rather than the content body. Furthermore, we explored three methods to organize personal Web information: 1) top-down statistical clustering; 2) salience phrase based clustering; and 3) support vector machine (SVM) based classification. Finally, we conducted a usability study to verify the effectiveness of our proposed solution. The experimental results demonstrated that users could visit the pages that have been browsed previously more easily with our approach than existing solutions.