1. Designing Search for Humans Dr. Marti Hearst UC Berkeley Enterprise Search Summit May 11 2010

2. Consider the Human Feelings Language, Memory, and Planning Sociability

3. Shutterstock: http://www.faqs.org/photo-dict/phrase/3404/emoticons.html

4. Feelings Aesthetics Emotional Stages Flow

5. 5 Feelings: The Importance of Aesthetics  With an aesthetically pleasing design:  People will enjoy working with it more  People will persist searching longer  People will choose it even if it is less efficient Nakarada-Kordic & Lobb, 2005, Ben-Basset et al. 2006, Parush et al. 1998, van der Heijden 2003

7. 7 Feelings: The Importance of Aesthetics  Small details matter  A left hand side line vs. a box for ads  The line integrates the results into the page  Balancing white space with content  Balancing font color, shape, and weight Hotchkiss 2007

8. 8 Feelings Kuhlthau on informational AND emotional stages in search (Assuming novice researchers engaged in challenging tasks) Uncertainty and apprehension Optimism (after deciding) Confusion, uncertainty, doubt, frustration Confidence dawning * Confidence growing Relief and satisfaction (or disappointment) Initiation Selection Exploration Formulation Collection Presentation

9. 9 Feelings: The Importance of Flow

10. 10 Feelings: The Importance of Flow From Csikszentmihalyi, M. (1991). Flow: The Psychology of Optimal Experience. HarperCollins via Bederson, Interfaces for staying in the flow, ACM Ubiquity 5(7), 2004

11. 11 Properties of Interfaces with Flow Inviting Support interrupt-free engagement in the task No blockages Easy reversal of actions Next steps seem to suggest themselves

13. Language, Memory, & Planning Address Anchoring and Vocabulary Problems Provide Memory Aids Suggest Helpful Next Steps

14. Language

15. 15 Language: The Vocabulary Problem  There are many ways to say the same thing.  People remember the gist but not the actual words used.

16. 16 Language: The Vocabulary Problem  With no other context, people generate different words for the same concepts.  The probability that two typists would suggest the same word for a given function:.11  The probability that two college students would name an object using the same word:.12. Furnas et al., 1987

17. 17 Language: The Problem of Anchoring  Try this experiment:  Tell people to think of the last 2 digits of their SSN  Then have them bid on something in auction  The SSN numbers they thought of influences their bids. Ariely, Predictably Irrational, 2008, Harper

18. 18 The Problem of Anchoring  Anchoring in search  A user starts with a set of words, then anchors on them  Harry Potter and the Half-Blood Prince sales  Harry Potter and the Half-Blood Prince amount sales  Harry Potter and the Half-Blood Prince quantity sales  Harry Potter and the Half-Blood Prince actual quantity sales  Harry Potter and the Half-Blood Prince sales actual quantity  Harry Potter and the Half-Blood Prince all sales actual quantity  all sales Harry Potter and the Half-Blood Prince  worldwide sales Harry Potter and the Half-Blood Prince  The opposite of the Vocabulary Problem! Russell, 2006

19. Provide Memory Aids Support “Recognition Over Recall”

20. 20 Provide Memory Aids Suggest the Search Action in or near the Query Form www.yelp.com, www.powerset.com

21. 21 Memory Aids Provide Access to Recent Actions PubMed amazon.com Dumais et al., Stuff I’ve Seen, SIGIR 2003

22. 22 Memory Aids; Anchoring Aids Dynamic Query Suggestions http://netflix.com http://google.com

23. 23 Memory Aids; Anchoring Aids Augment suggestions with images or faceted classes. http://www.imamuseum.org/ http://nextbio.com

24. 24 Suggest Next Steps: Query suggestions Show suggestions after the query has been issued. http://bing.com http://yahoo.com

25. 25 Suggest Next Steps: Query suggestions http://nextbio.com PubMed

26. 26 Suggest Next Steps: Query Destinations  Recorded search sessions for 100,000’s of users  For a given query, where did the user end up?  Users generally browsed far from the search results page (~5 steps)  On average, users visited 2 unique domains during the course of a query trail, and just over 4 domains during a session trail  Show the query trail endpoint information at query reformulation time  Query trail suggestions were used more often (35.2% of the time) than query term suggestions. White et al., SIGIR 2007

27. 27 Suggest Next Steps: Related Documents In some circumstances, related items work well PubMed amazon.com

28. 28 Putting It All Together: Faceted Navigation  Suggests next steps  Helps with Vocabulary Problem and Anchoring Problem  Promotes Flow  Show users structure as a starting point, rather than requiring them to generate queries  Organize results into a recognizable structure  Eliminates empty results sets

29. 29 A New Development: Faceted Breadcrumbs Nudelman, http://www.boxesandarrows.com/view/faceted-finding-with

31. Sociability People are Social; Computers are Lonely. Don’t Personalize Search, Socialize it!

32. 32 Social Search Implicit: Suggestions generated as a side-effect of search activity. Asking: Communicating directly with others. Collaboration: Working with other people on a search task. Explicit: knowledge accumulates via the actions of many.

33. 33 Social Search: Asking What do people ask of their social networks? Type%Example Recommendation29% Building a new playlist – any ideas for good running songs? Opinion22% I am wondering if I should buy the Kitchen-Aid ice cream maker? Factual17% Anyone know a way to put Excel charts into LaTeX? Rhetorical14% Why are men so stupid? Invitation9% Who wants to go to Navya Lounge this evening? Favor4% Need a babysitter in a big way tonight… anyone?? Social connection3% I am hiring in my team. Do you know anyone who would be interested? Offer1% Could any of my friends use boys size 4 jeans? Morris et al., CHI 2010

34. 34 Social Search: Implicit Suggestions  Human-generated suggestions still beat purely machine-generated ones  Spelling suggestions  Query term suggestions  Recommendations of book, movies, etc  Ranking (clickthrough statistics)

35. 35 Social Search: Explicit Help Question-Answering Sites  Content produced in a manner amenable to searching for answers to questions.  Search tends to work well on these sites and on the internet leading to these sites  This suggests that for the intranet, content is best generated and written this way.  Like an FAQ but with many authors and with the questions that the audience really wants the answers to.

36. 36

37. 37 Explicit Suggestions: Building Knowledge  Social knowledge management tools seem promising  Utilize the best of social networks, tagging, blogging, web page creation, wikis, and search. Millen et al., CHI 2006

38. 38 Collaborative Search Pickens et al., SIGIR 2008

39. 39 Summary: Consider the Human 1.Feelings  Emotional responses to information seeking  Aesthetics  Flow 2.Language / Memory / Planning  Scaffold memory by suggesting next steps, providing context and feedback  Tools to aid with the anchoring and the vocabulary problems 3.Sociability  Search as a social experience  Turning to others for certain types of task  Sharing information for next-generation knowledge management

40. Thank you! Buy it here! Full text freely available at: http://searchuserinterfaces.com