Ranking web search results from personalized perspective

Wen-Chih Peng*, Yu Chin Lin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations


In this paper, we exploit the technique of data mining to mine frequent access patterns from user browsing behavior. In light of frequent access patterns, we develop a solution procedure to automatically extract user interests. Furthermore, in accordance with user interests mined and feedbacks of users, we propose a new algorithm with the idea of dynamically adjusting the ranking scores of Web pages. Specifically, algorithm PPR (standing for Personalized PageRank), is divided into four phases. The first phase assigns the initial weights based on user interests. In the second phase, the virtual links and hubs are created according to user interests. By observing user click streams, our proposed algorithm will incrementally reflect user favors for the personalized ranking in the third phase. To improve the accuracy of ranking, collaborative filtering is taken into consideration when the new query is submitted. By conducting simulation experiments, we have shown that algorithm PPR is not only very effective but also very adaptive in providing personalized ranking to users.

Original languageEnglish
Title of host publicationProceedings - CEC/EEE 2006
Subtitle of host publicationJoint Conference - 8th IEEE International Conference on E-Commerce and Technology (CEC 2006), 3rd IEEE International Conference on Enterprise Computing, E-Commerce
StatePublished - 1 Dec 2006
EventCEC/EEE 2006 Joint Conferences - San Francisco, CA, United States
Duration: 26 Jun 200629 Jun 2006

Publication series

NameCEC/EEE 2006 Joint Conferences


ConferenceCEC/EEE 2006 Joint Conferences
CountryUnited States
CitySan Francisco, CA


  • Data mining
  • Personalization
  • Web mining

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