1. Topic Sensitive PageRank-Review

3. Determine important Hub pages and important Authority pages

4. + Query specific rank score

6. Assigns a priori “importance” estimates to pages

7. + Query independent rank score

9. One PageRank score per basis topic

10. + Query specific rank score

11. + Make use of context

14. Web W

15. Basis topic [ c1, ……c16] use 16 categories (first level of ODP)

16. Output

17. List of rank vectors[ r1, ……r16]

18. rj : (page -> page importance of topic cj)

20. Use a multinomial Naïve Bayes classifier

21. Training set : pages listed in ODP

22. Input : query or query, context

25. A page was relevant if 3of the 5 users’ judged it to be relevant. Training set : 280,000 of the 3 million URLs in the ODP

26. Experiment The average precision for rankings induced by topic-sensitive PageRank scoresis substantially higher than that of the unbiased PageRank.

28. Discussion Why author chosen ODP? Rocchio K-Nearest Neighbor Decision Tree Naïve Bayes Support Vector Machine We don’t need the name of topic too much.

30. Discussion What if there are relevant document in topics which have 0 point? If A document does not relate with shopping I can not retrieve any document in shopping. Calculate how similar each topic is and cover 0 point