Is Search Broken?!

1. Is Search Broken?! Daniel Tunkelang Chief Scientist, Endeca

2. howdy! • 1992: Bachelor’s + Master’s from MIT in CS + Math • 1998: PhD from CMU in CS (ACO program) • 1999: Co-founded Endeca! • 2008: ???

3. overview • Who is Endeca? • Is search broken? • If it is, what can we do about it?

4. who / what is endeca? • Software to help people explore, analyze, and understand complex information, guiding them to unexpected insights and better decisions. • 500+ customers • $108M revenue in 2007.

5. some of our customers

6. Is search broken?

7. Search has hit a wall.

8. search hits a wall in ecommerce

9. search hits a wall in knowledge management Current Search: it outsourcing

10. search even hits a wall on the web Results 1-10 out of about 344,000,000 for ir

11. But is search broken?

12. the accountants don’t think so

13. most users don’t think so 75

14. or do they? 78% wish search engines could read their minds. What frustrates users most? • 25%: deluge of results • 24%: too many paid listings • 19%: inability to understand their keywords • 19%: disorganized / random results The State of Search Autobytel & Kelton Research, Oct ’07

15. web search vs. enterprise search “Search on the internet is solved.I always find what I need.But why not in the enterprise? Seems like a solution waiting to happen.” - a Fortune 500 CTO

16. Can theory help?

17. relevant documents retrieved documents precision = fraction of retrieved documents that are relevant recall = fraction of relevant documents that are retrieved

18. why improve precision? the truth,nothing but the truth

19. why improve recall? the whole truth,

20. what we want… the truth,the whole truth,nothing but the truth

21. but there is a trade-off… precision recall

22. which should we favor? Precision…to avoid annoying users with irrelevant results? Recall…to make sure we don’t throw away results the user wants / needs?

23. Enough stalling…what’s the answer?!

24. depends on what you want vs.

25. you get what you pay for • There are easy use cases… • 30% of queries are navigational. • 30% of queries lead to Wikipedia pages. • Users won’t pay, but advertisers will! • …and hard use cases. • Queries where recall matters. • Exploratory search. • Enterprises will pay for insight.

26. Great, bring on the insight!

27. technology alone can’t provide insight • The system can’t read your mind. • Your spouse / best friend can’t read your mind. • Sometimes you can’t read your own mind.

28. So should we just give up?

29. technology is a catalyst • Computers are good at analysis. • People are good at using what they know. • How do we get the best of both worlds?

30. with apologies to luis von ahn

31. human-computer information retrieval • Instead of guessing the user’s intent,optimize communication. • De-emphasize the top ten documents;response is a set of documents. • Think beyond single queries;support refinement and exploration.

32. hcir cheats the trade-off precision recall

33. But how do we implement HCIR?

34. endeca's approach: guided summarization • Set retrieval that responds to queries with • an overview of the user's current context. • an organized set of options for incremental exploration. • Contextual summaries of document sets optimize system’s communication with user. • Query refinement options optimize user’s communication with system.

35. guided summarization for ecommerce Matching Categories include: Appliances > Small Appliances > Irons & Steamers Appliances > Small Appliances > Microwaves & Steamers Bath > Sauna & Spas > Steamers Kitchen > Bakeware & Cookware > Cookware > Open Stock Pots > Double Boilers & Steamers Kitchen > Small Appliances > Steamers

36. guided summarization for KM

37. Guided summarization starts withfaceted search.

38. facets 101

39. But faceted search isn’t enough…

40. showing the right facets: microwaves vs.

41. showing the right facets: ceiling fans

42. traditional topic taxonomy

43. dynamic topic facet Subject Artificial intelligence(227)High performance computing(244) Automatic theorem proving(9)History(11) Client/server computing(185)Information technology(145) Computer algorithms(110)Java(77) Computer architecture(162)Law and legislation(70) Computer networks(552)Logic, Symbolic and mathematical(16) Computer programs(139)Mathematics(70) Computer security(151)Mobile communication systems(54) Computer software(253)Operating systems(87) Computers(124) Parallel processing(619) Database management(277)Research(83) Distributed processing(937)Software engineering(197) Electronic data processing(1002)Supercomputers(139) Electronic digital computers(148)Web databases(54) Fault-tolerant computing(365)Wireless communication systems(97) Subject Electronic data processing (1002) Distributed processing (937) Parallel processing (619) Computer networks (562) Fault-tolerant-computing (365) Show more…

44. facets populated using entity extraction apple production

45. cutting through facets to show the big picture Search: storage

46. summarization: more than search and browse

47. guided summarization – a summary Guided summarization enables a dialog between the user and the data, enabling exploration and discovery.

48. The Moral

49. think outside the box • Search works for many use cases. • But not for some of the most valuable ones. • Focus on human-computer information retrieval.

50. One More Thing