TY - GEN
T1 - Personalized PageRank for web page prediction based on access time-length and frequency
AU - Guo, Yong Zhen
AU - Ramamohanarao, Kotagiri
AU - Park, Laurence A.F.
PY - 2007
Y1 - 2007
N2 - 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 than other methods that use only one of these two factors.
AB - 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 than other methods that use only one of these two factors.
UR - https://www.scopus.com/pages/publications/48349115487
U2 - 10.1109/WI.2007.4427174
DO - 10.1109/WI.2007.4427174
M3 - Conference Paper
AN - SCOPUS:48349115487
SN - 0769530265
SN - 9780769530260
T3 - Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, WI 2007
SP - 687
EP - 690
BT - Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, WI 2007
T2 - IEEE/WIC/ACM International Conference on Web Intelligence, WI 2007
Y2 - 2 November 2007 through 5 November 2007
ER -