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Improving Web Search Results Using Affinity Graph

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. Outline. Motivation Objective Definition Methods (Affinity Ranking) Experiments Conclusion Opinion. Motivation.

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Improving Web Search Results Using Affinity Graph

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  1. 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 SIGIR

  2. Outline • Motivation • Objective • Definition • Methods (Affinity Ranking) • Experiments • Conclusion • Opinion 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 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. 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. 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. d2 d3 d1 d4 SIGIR d5 d6

  7. Methods(cont.) • Information richness : • 1st • 2nd SIGIR

  8. Methods(cont.) • Diversity penalty : • 1st : • 2nd • 3rd , • 4th • 5th 2nd • 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 : SIGIR

  9. Experiments (In Yahoo & ODP) • Affinity Ranking vs. K-Means Clustering SIGIR

  10. Experiments (cont.) SIGIR

  11. Experiments (cont.) 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. SIGIR

  13. Experiments (cont.) • Improve within Top 50 Search Results SIGIR

  14. Experiments (cont.) SIGIR

  15. Experiments (α & β) SIGIR

  16. A Case Study • Outlook print error : 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. SIGIR

  18. Opinion • Future work : scaling the AR computation, to the Web scale. SIGIR

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