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Personalized Query Expansion for the Web

Personalized Query Expansion for the Web. Paul- Alexandru Chirita , Claudiu S. Firan , Wolfgang Nejdl. Gabriel Barata. Motivation. by Tojosan @ Flickr. What is query expansion?. Add meaningful search terms to the query…. What is PIR based query expansion?.

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Personalized Query Expansion for the Web

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  1. Personalized Query Expansion for the Web Paul-AlexandruChirita, Claudiu S. Firan, Wolfgang Nejdl Gabriel Barata

  2. Motivation byTojosan @ Flickr

  3. What is query expansion? Add meaningful search terms to the query…

  4. What is PIR based query expansion? Add meaningful search terms to the query… … related to the use’s interests.

  5. Why PIR based query expansion? More personalization quality! More privacy!

  6. Example Google search: “canon book”

  7. Example Top 3 results: • The Canon: A Whirligig Tour of the Beautiful Basics of Science (Hardcover) @ Amazon • Western Canon @ Wikipedia • Biblical Canon @ Wikipedia

  8. Example Top 3 results: • The Canon: A Whirligig Tour of the Beautiful Basics of Science (Hardcover) @ Amazon • Western Canon @ Wikipedia • Biblical Canon @ Wikipedia

  9. Example Expanded query: “canon book bible”

  10. Example Top 3 results: • Biblical Canon @ Wikipedia • Books of the Bible @ Wikipedia • The Canon of the Bible @ catholicapologetics.org

  11. Query Expansion using Desktop data by Old Shoe Woman @ Flickr

  12. Algorithms • Expanding with Local Desktop Analysis • Expanding with Global Desktop Analysis

  13. Algorithms • Expanding with Local Desktop Analysis • Expanding with Global Desktop Analysis

  14. Expanding with Local Desktop Analysis • Term and Document Frequency • Lexical Compounds • Sentence Selection

  15. Expanding with Local Desktop Analysis • Term and Document Frequency • Lexical Compounds • Sentence Selection

  16. Term and Document Frequency

  17. Expanding with Local Desktop Analysis • Term and Document Frequency • Lexical Compounds • Sentence Selection

  18. Lexical Compounds { adjective? Noun+ }

  19. Expanding with Local Desktop Analysis • Term and Document Frequency • Lexical Compounds • Sentence Selection

  20. Sentence Selection

  21. Expanding with Global Desktop Analysis • Term Co-occurrence Statistics • Thesaurus based Expansion

  22. Expanding with Global Desktop Analysis • Term Co-occurrence Statistics • Thesaurus based Expansion

  23. Term Co-occurrence Statistics

  24. Expanding with Global Desktop Analysis • Term Co-occurrence Statistics • Thesaurus based Expansion

  25. Thesaurus based Expansion

  26. Experiments & Evaluation by Canadian Museum of Nature @ Flickr

  27. Experiments • 18 users • Files indexed within user selected paths, Emails and Web cache

  28. Experiments • They chose 4 queries: • 1 from the top 2% log queries (avg. length = 2.0) • 1 random log query (avg. length = 2.3) • 1 self-selected specific query (avg. length = 2.9) • 1 self-selected ambiguous query (avg. length = 1.8)

  29. Evaluation

  30. Evaluation • Evaluated algorithms: • Google: Google query output • TF, DF: Term and Document Frequency • LC, LC[O]: Regular and Optimized Lexical Compounds • TC[CS], TC[MI], TC[LR]: Term Co-occurrences Statistics using Cosine Similarity, Mutual Information and Likelihood Ratio • WN[SYN], WN[SUB], WN[SUP]: WordNet based expansion with synonyms, sub-concepts and super-concepts.

  31. Results Log queries:

  32. Results Self-selected queries:

  33. Introducing Adaptativity by RavenCore17 @ Flickr

  34. Query Clarity

  35. Adaptive Expansion

  36. Experiments • Same experimental setup as for the previous analyzis.

  37. Results Log queries:

  38. Results Self-selected queries:

  39. Results

  40. Conclusions by ThisIsIt2 @ Flickr

  41. Conclusions • Five techniques for determining expansion terms from personal documents. • Empirical analysis showed that these approaches perform very well. • Expansion process adapts accordingly to query features. • Adaptive expansion process proved to yield significant improvements over the static one.

  42. End Any questions?

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