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

2. Motivation

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

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

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

26. Experiments & Evaluation

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. 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

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

41. 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?