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Personalized Query Expansion for the Web

This paper explores personalized query expansion methods to enhance search relevance and user experience. It discusses traditional query expansion techniques and introduces a user-centric approach that utilizes Personal Interest Representation (PIR) to add meaningful terms based on users' interests. The study demonstrates how enhanced algorithms can improve search results, privacy, and personalization quality. By comparing different expansion techniques, including term co-occurrences and lexical compounds, the authors provide insights and empirical results that highlight the effectiveness of adapting query expansion to individual user needs and behaviors.

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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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