Designing Search For Humans
Upcoming SlideShare
Loading in...5
×
 

Like this? Share it with your network

Share

Designing Search For Humans

on

  • 7,118 views

 

Statistics

Views

Total Views
7,118
Views on SlideShare
6,684
Embed Views
434

Actions

Likes
12
Downloads
197
Comments
4

14 Embeds 434

http://thenoisychannel.com 285
http://www.slideshare.net 55
http://ecm-stuff.blogspot.com 54
http://ecm-stuff.blogspot.fr 20
http://blog.contentmanagementconnection.com 7
http://static.slidesharecdn.com 3
http://webcache.googleusercontent.com 2
http://cmc.l50sw.com 2
http://www.ecm-stuff.blogspot.ca 1
http://ecm-stuff.blogspot.gr 1
http://ecm-stuff.blogspot.com.br 1
http://ecm-stuff.blogspot.co.nz 1
http://www.ecm-stuff.blogspot.com 1
http://ecm-stuff.blogspot.hk 1
More...

Accessibility

Categories

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
  • Best one
    Hope you are in good health. My name is AMANDA . I am a single girl, Am looking for reliable and honest person. please have a little time for me. Please reach me back amanda_n14144@yahoo.com so that i can explain all about myself .
    Best regards AMANDA.
    amanda_n14144@yahoo.com
    Are you sure you want to
    Your message goes here
    Processing…
  • Great presentation - I especially liked the incorporation of flow.
    Are you sure you want to
    Your message goes here
    Processing…
  • Can you explain why you say this? I only removed a few images that were there just for decoration.
    Are you sure you want to
    Your message goes here
    Processing…
  • Too bad half of the presentation is missing.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

Designing Search For Humans Presentation Transcript

  • 1. Designing Search for Humans Dr. Marti Hearst UC Berkeley Enterprise Search Summit Keynote Speech May 11 2010
  • 2. Consider the Human Feelings Language, Memory, and Planning Sociability
  • 3. Feelings Aesthetics Emotional Stages Flow
  • 4. 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
  • 5.  
  • 6. 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
  • 7. 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
  • 8. Feelings: The Importance of Flow
  • 9. 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
  • 10. 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
  • 11. Language, Memory, & Planning Address Anchoring and Vocabulary Problems Provide Memory Aids Suggest Helpful Next Steps
  • 12. Language
  • 13. Language: The Vocabulary Problem
    • There are many ways to say the same thing.
    • People remember the gist but not the actual words used.
  • 14. 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
  • 15. 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
  • 16. 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
    • Contrast with the Vocabulary Problem!
    Russell, 2006
  • 17. Provide Memory Aids Support “Recognition Over Recall”
  • 18. Provide Memory Aids
    • Suggest the Search Action in or near the Query Form
    www.yelp.com, www.powerset.com
  • 19. Memory Aids
    • Provide Access to Recent Actions
    PubMed amazon.com Dumais et al., Stuff I’ve Seen, SIGIR 2003
  • 20. Memory Aids; Anchoring Aids
    • Dynamic Query Suggestions
    http://netflix.com http://google.com
  • 21. Memory Aids; Anchoring Aids
    • Augment suggestions with images or faceted classes.
    http://www.imamuseum.org/ http://nextbio.com
  • 22. Suggest Next Steps: Query suggestions
    • Show suggestions after the query has been issued.
    http://bing.com http://yahoo.com
  • 23. Suggest Next Steps: Query suggestions http://nextbio.com PubMed
  • 24. 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
  • 25. Suggest Next Steps: Related Documents
    • In some circumstances, related items work well
    PubMed amazon.com
  • 26. 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
  • 27. A New Development: Faceted Breadcrumbs
    • Nudelman, http://www.boxesandarrows.com/view/faceted-finding-with
  • 28. Sociability People are Social; Computers are Lonely. Don’t Personalize Search, Socialize it!
  • 29. 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.
  • 30. The DARPA Network (Red Balloon) Challenge The ultimate in social question answering
  • 31. Social Search: Asking
    • What do people ask of their social networks?
    Morris et al., CHI 2010 Type % Example Recommendation 29% Building a new playlist – any ideas for good running songs? Opinion 22% I am wondering if I should buy the Kitchen-Aid ice cream maker? Factual 17% Anyone know a way to put Excel charts into LaTeX? Rhetorical 14% Why are men so stupid? Invitation 9% Who wants to go to Navya Lounge this evening? Favor 4% Need a babysitter in a big way tonight… anyone?? Social connection 3% I am hiring in my team. Do you know anyone who would be interested? Offer 1% Could any of my friends use boys size 4 jeans?
  • 32. 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)
  • 33. 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.
  • 34.  
  • 35. 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
  • 36. Collaborative Search Pickens et al., SIGIR 2008
  • 37. Summary: Consider the Human
    • Feelings
      • Emotional responses to information seeking
      • Aesthetics
      • Flow
    • Language / Memory / Planning
      • Scaffold memory by suggesting next steps, providing context and feedback
      • Tools to aid with the anchoring and the vocabulary problems
    • Sociability
      • Search as a social experience
      • Turning to others for certain types of task
      • Sharing information for next-generation knowledge management
  • 38. Thank you! Full text freely available at: http://searchuserinterfaces.com