Designing Search For Humans

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

Consider the Human Feelings Language, Memory, and Planning Sociability

Feelings Aesthetics Emotional Stages Flow

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

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

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

Feelings: The Importance of Flow

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

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

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

Language

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

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

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

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

Provide Memory Aids Support “Recognition Over Recall”

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

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

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

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

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

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

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

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

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

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

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

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.

The DARPA Network (Red Balloon) Challenge The ultimate in social question answering

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?

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)

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.

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

Collaborative Search Pickens et al., SIGIR 2008

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

Thank you! Full text freely available at: http://searchuserinterfaces.com

http://thenoisychannel.com	279
http://www.slideshare.net	55
http://ecm-stuff.blogspot.com	54
http://ecm-stuff.blogspot.fr	19
http://blog.contentmanagementconnection.com	7
http://static.slidesharecdn.com	3
http://webcache.googleusercontent.com	2
http://cmc.l50sw.com	2
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.gr	1

Designing Search For Humans

by marti_hearst

Accessibility

Categories

Upload Details

Usage Rights

12 Embeds 425

Statistics

Designing Search For Humans Presentation Transcript