A Comparison of Implicit and Explicit Links for Web Page Classification

• Dou Shen, Department of Computer Science, Hong Kong University of Science and Technology, P.R. China
• Jian-Tao Sun, Microsoft Research Asia, P.R. China
• Qiang Yang, Department of Computer Science Hong Kong University of Science and Technology, China
• Zheng Chen, Microsoft Research Asia, P.R. China

Full text:

Track: Data Mining

Slot: 14:00-15:30, Friday 26th May

It is well known that Web-page classification can be enhanced by using hyperlinks that provide linkages between Web pages. However, in the Web space, hyperlinks are usually sparse, noisy and thus in many situations can only provide limited help in classification. In this paper, we extend the concept of linkages from explicit hyperlinks to implicit links built between Web pages. By observing that people who search the Web with the same queries often click on different, but related documents together, we draw implicit links between Web pages that are clicked after the same queries. Those pages are implicitly linked. We provide an approach for automatically building the implicit links between Web pages using Web query logs, together with a thorough comparison between the uses of implicit and explicit links in Web page classification. Our experimental results on a large dataset confirm that the use of the implicit links is better than using explicit links in classification performance, with an increase of more than 10.5% in terms of the Macro-F1 measurement.

Other items being presented by these speakers

• Mining Clickthrough Data for Collaborative Web Search (Posters Track)
• CWS: A Comparative Web Search System (Search Track)