Workshop proceeding - final.pdf - Faculty of Information and ...

Web Page Prediction Based on Conditional Random Fields
* Presenter
1. The University of Melbourne
Yong Zhen Guo 1 * , Yuan Miao 1
Web page prefetching is used to reduce the access latency on the Internet. However, if
most prefetched Web pages are not visited by the users in their subsequent accesses, the
limited network bandwidth and server resources will not be used efficiently and may even
worsen the access delay problem. Therefore, enhancing the Web page prediction accuracy is a
key issue of Web page prefetching. In this talk, a Web page prediction method based on the
powerful sequential learning model, Conditional Random Fields (CRFs), is proposed to
improve the Web prediction accuracy. We also show how to scale the CRF-based Web
prediction method to large-size websites by using the ECOC (Error Correcting Output
Coding) technique. Moreover, because the limited class information provided to the binarylabel
sub-classifiers in ECOCCRFs will also lead to inferior accuracy when compared to the
multi-label CRFs, in this talk, we introduce the grouping method which allows us to obtain a
prediction accuracy closer to that of multi-label CRFs while maintaining the advantage of
ECOC-CRFs. The experimental results show that the Web prediction method based on the
grouped ECOC-CRFs is highly accurate and scalable, and is ready for use in large-scale
websites to perform predictions.
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