Web Page Prediction Based on Conditional Random Fields
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Web users access paths clustering based on possibilistic and fuzzy sets theory
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
ClickRank: Learning Session-Context Models to Enrich Web Search Ranking
ACM Transactions on the Web (TWEB)
Web Page Prediction by Clustering and Integrated Distance Measure
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Efficient ad-hoc search for personalized PageRank
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
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Web page prefetching techniques are used to address the access latency problem of the Internet. To perform successful prefetching, we must be able to predict the next set of pages that will be accessed by users. The PageRank algorithm used by Google is able to compute the popularity of a set of Web pages based on their link structure. In this paper, a novel PageRank-like algorithm is proposed for conducting Web page prediction. Two biasing factors are adopted to personalize PageRank, so that it favors the pages that are more important to users. One factor is the length of time spent on visiting a page and the other is the frequency that a page was visited. The experiments conducted show that using these two factors simultaneously to bias PageRank results in more accurate Web page prediction