1. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Key Blog Distillation: Ranking Aggregates Presenter : Yu-hui Huang Authors :Craig Macdonald, Iadh Ounis CIKM 2009 國立雲林科技大學 National Yunlin University of Science and Technology 1

2. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Outline Motivation Objective Methodology Experiments Conclusion Comments 2

3. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Motivation Help user to look for blogs that interest them. What is importance degree for each search result. 3

4. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Objective To provide key blogs relevant to the query topic area. Ranking the blog according to degree of importance. Blog distillation ： could be add search feet to directories or return suggest for his/her RSS. 4

5. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Ranking ： 5

6. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Ranking ： 6

7. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Weight function 7 qtw ： don’t ask me 

8. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Three hypotheses: to model more fully the definition of a relevant blog given to the assessors. Central Interest: If the posts of each blog are clustered, then relevant blogs will have blog posts about the topic in one of the larger clusters. Recurring Interest: Relevant blogs will cover the topic many times across the timespan of the collection. Focused Interest: Relevant blogs will mainly blog around a central topic area - i.e. they will have a coherent language model with which they blog. 8

9. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Central interest ： (quality score) 9 cluster(p;B) is the rank of the cluster in which post p occurred for blog B (largest cluster has rank 1).

10. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Recurring interest ： 10

11. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Focused interest ： 11

12. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments 12

13. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Mean Average Precision (MAP) Mean Reciprocal Rank(MRR) P @ rank 10 ： precision @ rank 10  direct translated into Chinese please 13

14. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments 14

15. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Conclusion 15 Add normalization component to the voting techniques that could indeed improve the retrieval performance. Authors consider that using the XML content will reduce the amount of noise. (god)

16. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Comments Advantage  … Drawback  This paper is non detail  Can description for example Application  Search engine (maybe) 16