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Enhanced Webpage Prediction Using Rank Based Feedback Process

In: New Trends in Computational Vision and Bio-inspired Computing

Author

Listed:
  • K. Shyamala

    (Dr. Ambedkar Government Arts College (Autonomous), Affiliated to University of Madras, PG & Research Department of Computer Science)

  • S. Kalaivani

    (Dr. Ambedkar Government Arts College (Autonomous), Affiliated to University of Madras, PG & Research Department of Computer Science)

Abstract

In recent days, user perceived latency has become a significant performance problem in the World Wide Web. The poor performance of the website is the important reason for the visitors to quit from the site. This reason may lead to loss of revenue for the e-commerce websites. In this paper, an attempt has been made to minimize web user perceived latency by predicting and prefetching the users’ future requested pages. The present work shows the Enhanced Monte Carlo Prediction (EMCP) algorithm by examining and including a rank for each predicted pages through feedback process from the most recent user navigation. Here, Webpages are predicted dynamically, (i.e.) the graph constructed in the work will refresh automatically whenever new pages are added to the website. Experimental results shows that better accuracy has been given by the rank based feedback prediction algorithm.

Suggested Citation

  • K. Shyamala & S. Kalaivani, 2020. "Enhanced Webpage Prediction Using Rank Based Feedback Process," Springer Books, in: S. Smys & Abdullah M. Iliyasu & Robert Bestak & Fuqian Shi (ed.), New Trends in Computational Vision and Bio-inspired Computing, pages 567-576, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-41862-5_55
    DOI: 10.1007/978-3-030-41862-5_55
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