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Referrer graph: a low-cost web prediction algorithm

Published: 22 March 2010 Publication History

Abstract

This paper presents the Referrer Graph (RG) web prediction algorithm as a low-cost solution to predict next web user accesses. RG is aimed at being used in a real web system with prefetching capabilities without degrading its performance. The algorithm learns from user accesses and builds a Markov model. These kinds kind of algorithms use the sequence of the user accesses to make predictions. Unlike previous Markov model based proposals, the RG algorithm differentiates dependencies in objects of the same page from objects of different pages by using the object URI and referrer in each request. This permits us to build a simple data structure that is easier to handle and, consequently, with a lower computational cost in comparison with other algorithms. The RG algorithm has been evaluated and compared with the best prediction algorithms proposed in the open literature, and the results show that it achieves similar precision values and page latency savings but requiring much less computational and memory resources.

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  • (2024)Statistical analysis of user access graph from CiNii logsCiNii のログから見るユーザーアクセスグラフの計量分析Joho Chishiki Gakkaishi10.2964/jsik_2024_00634:2(113-120)Online publication date: 25-May-2024
  • (2021)Assessing the Feasibility of Web-Request Prediction Models on Mobile Platforms2021 IEEE/ACM 8th International Conference on Mobile Software Engineering and Systems (MobileSoft)10.1109/MobileSoft52590.2021.00008(12-23)Online publication date: May-2021
  • (2018)Revealing connectivity structural patterns among web objects based on co-clustering of bipartite request dependency graphWireless Networks10.1007/s11276-016-1345-524:2(439-451)Online publication date: 1-Feb-2018
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      cover image ACM Conferences
      SAC '10: Proceedings of the 2010 ACM Symposium on Applied Computing
      March 2010
      2712 pages
      ISBN:9781605586397
      DOI:10.1145/1774088
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      Published: 22 March 2010

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      Author Tags

      1. latency reduction
      2. web prediction
      3. web prefetching

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      March 22 - 26, 2010
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      SAC '10 Paper Acceptance Rate 364 of 1,353 submissions, 27%;
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      View all
      • (2024)Statistical analysis of user access graph from CiNii logsCiNii のログから見るユーザーアクセスグラフの計量分析Joho Chishiki Gakkaishi10.2964/jsik_2024_00634:2(113-120)Online publication date: 25-May-2024
      • (2021)Assessing the Feasibility of Web-Request Prediction Models on Mobile Platforms2021 IEEE/ACM 8th International Conference on Mobile Software Engineering and Systems (MobileSoft)10.1109/MobileSoft52590.2021.00008(12-23)Online publication date: May-2021
      • (2018)Revealing connectivity structural patterns among web objects based on co-clustering of bipartite request dependency graphWireless Networks10.1007/s11276-016-1345-524:2(439-451)Online publication date: 1-Feb-2018
      • (2016)Exploration of Web Page Structural Patterns Based on Request Dependency Graph DecompositionInternational Journal of Digital Crime and Forensics10.4018/IJDCF.20161001018:4(1-13)Online publication date: 1-Oct-2016
      • (2016)Request Dependency Graph: A Model for Web Usage Mining in Large-Scale Web of ThingsIEEE Internet of Things Journal10.1109/JIOT.2015.24529643:4(598-608)Online publication date: Aug-2016
      • (2016)Predicting touch operations by using hover information in smartphones for data prefetching2016 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)10.1109/ICMEW.2016.7574697(1-6)Online publication date: Jul-2016
      • (2012)Key factors in web latency savings in an experimental prefetching systemJournal of Intelligent Information Systems10.1007/s10844-011-0188-x39:1(187-207)Online publication date: 1-Aug-2012
      • (2011)Optimizing Predictive Prefetching in Multi-client Single-Server EnvironmentProceedings of the 2011 Frontiers of Information Technology10.1109/FIT.2011.36(154-160)Online publication date: 19-Dec-2011

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