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Effective Time Ratio: A measure for Web search engine with document snippet

Effective Time Ratio: A measure for Web search engine with document snippet. Outline. Introduction Effective Time Ratio Experiment and Results Conclusion. Existing IR Measures. search engine quality. doc relevance. Snippets. Snippets : False Positive.

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Effective Time Ratio: A measure for Web search engine with document snippet

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  1. Effective Time Ratio:A measure for Web searchengine with document snippet

  2. Outline • Introduction • Effective Time Ratio • Experiment and Results • Conclusion

  3. Existing IR Measures search engine quality doc relevance

  4. Snippets

  5. Snippets:False Positive Waste time examining irrelevant document!

  6. Snippets:False Negative Miss really relevant document!

  7. Snippets Affect the Search Engine Quality!

  8. Outline • Introduction • Effective Time Ratio • Experiment and Results • Conclusion

  9. IntuitionReduce time wasted on examining irrelevant information

  10. Effective Time Ratio (ETR) of getting relevant information of total search

  11. Precision:a special case of ETR • Assumption: constant time(T) on each doc P@5 = 0.4 of getting rel information = 2T of total search = 5T ETR@5 = 0.4

  12. User Behavior Assumptions • Examination Assumption[Dupret+08,Craswell+08, Guo+09a,Guo+09b] • Cascade Assumption[Craswell+08, Guo+09a,Guo+09b, Chapelle+09] • Examination Time Assumption • Snippet Examination Time: T1 • Doc Examination Time: T2 Position i Position (i+1)

  13. User Behavior

  14. ETR for Search Engine with Snippets of getting rel information T1 + T2 of total search 5T1 + 3T2

  15. Factors affecting ETR • Retrieval System Performance(P@N) • Snippet Quality • First Type Error(False Positive) • Second Type Error(False Negative) P1 = Pr( | ) P2 = Pr( | )

  16. Expected ETR • C = T1/T2

  17. Properties • It helps (increases ETS) by using perfect snippets (with no first/second type error)

  18. Properties • ETR can reflect snippet generation/retrieval system quality

  19. Outline • Introduction • Effective Time Ratio • Experiment and Results • Conclusion

  20. User Study Data • 10 Users • 50 Questions from commercial query log • 25 open + 25 close • Various topics/difficulty level • For each question, 100 top results(snippet + doc) from a search engine

  21. User Study • Each user examines the results to answer the question • Write the answer • Report the satisfaction degree • Three assessors judges each snippet/doc as relevant/irrelevant

  22. Measure Validation • Correlation[Huffman+07] between • User reported satisfaction • Measure value based on judgments • Higher correlation indicates the measure can reflect user satisfaction better

  23. Measures • Traditional IR measures • P@N, RR, DCG, CP • Version of measure including snippet quality [Turpin+09] • P’@N, RR’, DCG’, CP’ • ETR and its cumulated version • ETR and CETR

  24. Results • Up-bound: 0.706 (avg correlation between users) • Traditional Measures • Measures including snippet quality[Turpin+09] • ETR and its cumulated version

  25. Results • Open Questions • Close Questions

  26. Conclusion • Search engine quality is also affected by snippets • ETR can reflect both retrieval system and snippet generation algorithm quality • ETR can reflect user satisfaction better than existing measures

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