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大咪報告
大咪報告
大咪報告
大咪報告
大咪報告
大咪報告
大咪報告
大咪報告
大咪報告
大咪報告
大咪報告
大咪報告
大咪報告
大咪報告
大咪報告
大咪報告
大咪報告
大咪報告
大咪報告
大咪報告
大咪報告
大咪報告
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大咪報告

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Transcript

  • 1. A Web Usage Mining Approach Based on LCS algorithm in Online Predicting Recommendation Systems Author: Mehrad Jalali, Norwati Mustapha, Ali Mamat Reporter:Nai-Hung Cheng Date:2010/09/30
  • 2.
    • Introduction
    • Architecture overview
    • Classification Algorithm by LCS
    • Experiments
    • Colcusion
    Outline
  • 3. Intruction
    • Web Mining
      • Web content mining
      • Web usage mining
      • Web structure mining
    • Web Usage Mining
      • WUM is process of extracting knowledge from user’s accss data
      • The process of WUM can divide into online and off-line architecture
    • Novel approach for classifying user navigation patterns in offline by LCS algo
  • 4. Architecture overview
    • Architecture graph
  • 5. Architecture overview (cont.)
    • Off-line phase of the architecture
  • 6. Architecture overview (cont.)
    • Data Pretreatment
      • Date Collection
      • Data Cleaning
      • Session Identification
    • Navigation Pattern Mining
      • Navigation Pattern Modeling
      • Clustering Algo based on graph partition
  • 7. Architecture overview (cont.)
  • 8. Architecture overview (cont.)
      • Clustering Algo based on graph partition
      • We apply graph partitioning algorithm to finds groups of strongly correlated pages by partitioning the graph according to its connected components.
      • We start form a vertex by Depth First Search on the graph
      • After the component has been found, the algo will check if there any vertex which is not visited. If so, it means that a previously connected component has been split.
      • Before the clusters put into the navigation pattern profile, the clusters are ranked based on the values store in the matrix.
  • 9. Architecture overview (cont.)
    • Online phase of the architecture
  • 10. Architecture overview (cont.)
    • Prediction engine
      • The main objective of prediction engine in this part of architecture is to classify user navigation patterns and predicts user’s future requests. For this purpose we propose a novel approach to classify current user activity.
  • 11. Classification Algorithm by LCS
    • Longest Common Subsequence algorithm
  • 12. Classification Algorithm by LCS (cont.)
  • 13. Classification Algorithm by Longest Common Subsequence (cont.)
  • 14. Classification Algorithm by Longest Common Subsequence (cont.)
    • An Example of the classification algorithm
  • 15. An Example of the classification algorithm
  • 16. Experimental evaluation
    • K-fold cross-validation
      • We use 10-fold cross validation.
      • In each of the 10 iterations, the data set divided into training (90%) and evaluation (10%) datasets.
      • Each navigational pattern np in the evaluation set is divided into two parts.
      • The first part, n pageviews in np are used for generating predictions,whereas, the remaining part of np is used to evaluate the generated predictions.
  • 17. Experimental evaluation (cont.)
  • 18. Experimental evaluation (cont.)
  • 19. Experimental evaluation (cont.)
  • 20. Experimental evaluation (cont.)
  • 21. Conclusion In this paper, we advance an architecture and proposed a novel contribution to classify user navigation pattern and online predicting users’ future request by mining of web server logs. In this paper we used LCS algorithm for improving accuracy of recommendation. The Excremental results show that the approach can improve accuracy of classification in the architecture. For the future, we would perfect the architecture and let it serves for actual users to the best of its abilities.
  • 22. THE END

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