1. Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology SIGIR1 Improving Web Search Results Using Affinity Graph Advisor : Dr. Hsu Presenter : Jia-Hao Yang Author :Benyu Zhang, Hua Li, Yi Liu, Wensi Xi, Weiguo Fan

2. Intelligent Database Systems Lab N.Y.U.S.T. I. M. SIGIR 2 Outline  Motivation  Objective  Definition  Methods (Affinity Ranking)  Experiments  Conclusion  Opinion

3. Intelligent Database Systems Lab N.Y.U.S.T. I. M. SIGIR 3 Motivation  situation ─ Many of the queries are ambiguous. ─ the user’s information needs are unknown. Ex : “ 足球 ”, 是只想要足球還是要找足球賽  In traditional, precision and recall are two metrics, but these didn’t consider the content of documents. Hyperlink

4. Intelligent Database Systems Lab N.Y.U.S.T. I. M. SIGIR 4 Objective  Two metrics, diversity and information richness, have been proposed to improve this problem.  Re-ranking the top search results to satisfy the user’s information needs.

5. Intelligent Database Systems Lab N.Y.U.S.T. I. M. SIGIR 5 Definition  Diversity measures the variety of topics in a group of documents.  Information richness measures how many different topics a single document contains.

6. Intelligent Database Systems Lab N.Y.U.S.T. I. M. SIGIR 6 Methods  AG : According to vector space model, each document can be represented,  If we consider documents as nodes, the document collection can be modeled as a graph by generating the link between documents. d1d1 d5d5 d6d6 d4d4 d3d3 d2d2

7. Intelligent Database Systems Lab N.Y.U.S.T. I. M. SIGIR 7 Methods(cont.)  Information richness :  1 st  2 nd

8. Intelligent Database Systems Lab N.Y.U.S.T. I. M. SIGIR 8 Methods(cont.)  Diversity penalty :  1 st :  2 nd  3 rd,  4 th  5 th 2 nd  Re-ranking : ─ The score-combination scheme uses a linear combination of two parts: ─ The rank-combination scheme of re-ranking uses a linear combination of the ranks based on full-text search and Affinity Ranking :

9. Intelligent Database Systems Lab N.Y.U.S.T. I. M. SIGIR 9 Experiments (In Yahoo & ODP)  Affinity Ranking vs. K-Means Clustering

10. Intelligent Database Systems Lab N.Y.U.S.T. I. M. SIGIR 10 Experiments (cont.)

11. Intelligent Database Systems Lab N.Y.U.S.T. I. M. SIGIR 11 Experiments (cont.)

12. Intelligent Database Systems Lab N.Y.U.S.T. I. M. SIGIR 12 Experiments (In Newsgroup)  Improve in Top 10 Search Results :  As the top 10 search results always receive the most attention of end-users, we show how Affinity Ranking affects the top 10 search results from the newsgroup data set.

13. Intelligent Database Systems Lab N.Y.U.S.T. I. M. SIGIR 13 Experiments (cont.)  Improve within Top 50 Search Results

14. Intelligent Database Systems Lab N.Y.U.S.T. I. M. SIGIR 14 Experiments (cont.)

15. Intelligent Database Systems Lab N.Y.U.S.T. I. M. SIGIR 15 Experiments (α & β)

16. Intelligent Database Systems Lab N.Y.U.S.T. I. M. SIGIR 16 A Case Study  Outlook print error :

17. Intelligent Database Systems Lab N.Y.U.S.T. I. M. SIGIR 17 Conclusion  This paper proposed two new metrics, diversity and information richness, and a novel ranking scheme, Affinity Ranking, to measure the search performance.  By presenting wider topic coverage and more highly informative results in each topic in the top results, this method can effectively improve the search performance.

18. Intelligent Database Systems Lab N.Y.U.S.T. I. M. SIGIR 18 Opinion  Future work : scaling the AR computation, to the Web scale.