1. CSE 450 – Web Mining Seminar Professor Brian D. Davison Fall 2005 A Presentation on When Experts Agree: Using Non-Affiliated Experts to Rank Popular Topics K. Bharat & G. A. Mihaila WWW10 Conference, May 2001, Hong Kong by Osama Ahmed Khan 10/06/2005

2. Problem  Query on Popular Topic  Content Analysis Solution  Most Authoritative Pages

3. Technical Terms  Expert  Recommendation  Non-affiliation

4. Hilltop Algorithm 1.Expert Lookup  Detecting Host Affiliation  Expert Selection  Expert Indexing 2.Target Ranking  Computing Expert Score  Computing Target Score

5. Detecting Host Affiliation  Conditions  Same first 3 octets of IP 127.0.0.1127.0.0.15  Same rightmost non-generic token of hostname www.ibm.comwww.ibm.co.mx  Union-Find Algorithm

6. Expert Selection  Retrieve all webpages with: Out-degree > Threshold (k) (e.g. k = 5)  Expert will have: URLs pointing to k distinct non-affiliated hosts

7. Expert Indexing  Inverted Index  Mapping Keywords to Experts  Key Phrases  Match Positions

8. Computing Expert Score  Condition  Atleast 1 URL with all query keywords  Expert Score: (S 0, S 1, S 2 ) S i = SUM {key phrases p with k-i query terms} * LevelScore(p) * FullnessFactor(p,q) Expert_Score = 2 32 * S 0 + 2 16 * S 1 + S 2

9. Computing Target Score  Condition  Atleast 2 non-affiliated experts  Target Score: Edge_Score(E,T) = Expert_Score(E) * SUM {query keywords w} * occ(k,T) Target_Score = Sum {Edge_Score(E,T)}

10. Evaluation 1.Locating Specific Popular Targets

11. Evaluation Evaluation (Contd.) 2.Gathering Relevant Pages

12. Conclusion  Characteristics  Popular Queries  Expert Subset  Hilltop vs.  PageRank  Topic Distillation

13. Thank You