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Enhancing Web Search Results with Affinity Graph Advisor

This paper presents a novel approach to improving web search results by introducing two key metrics: diversity and information richness, alongside a new ranking scheme known as Affinity Ranking. The study highlights the challenges of ambiguous queries and the lack of user information needs, exemplified by questions like “足球” (football). Experiments demonstrate that incorporating Affinity Ranking enhances the variety of topics and the informativeness of search results, particularly within the top search listings. The findings suggest significant improvements in user satisfaction and search performance.

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Enhancing Web Search Results with Affinity Graph Advisor

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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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