1 Flexible Search and Navigation using Faceted Metadata Prof. Marti Hearst Dr. Rashmi Sinha, Ame Elliott, Jennifer English, Kirsten Swearingen, Ping Yee February, 2002 University of California, Berkeley http://bailando.sims.berkeley.edu/ flamenco.html Research funded by NSF CAREER Grant, NSF9984741
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1 Flexible Search and Navigation using Faceted Metadata Prof. Marti Hearst Dr. Rashmi Sinha, Ame Elliott, Jennifer English, Kirsten Swearingen, Ping Yee.
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Flexible Search and Navigation using Faceted
MetadataProf. Marti Hearst
Dr. Rashmi Sinha, Ame Elliott, Jennifer English, Kirsten Swearingen, Ping Yee
February, 2002University of California, Berkeley
http://bailando.sims.berkeley.edu/flamenco.htmlResearch funded by
NSF CAREER Grant, NSF9984741
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Outline
1. Motivation2. Approach
Integrate Search into Information Architecture via Faceted Metadata
4. Recipe Interface and Usability Study5. Image Interfaces and Usability Studies6. Conclusions
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Motivation and Background
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Claims
• Web Search is OK– Gets people to the right starting
points
• Web SITE search is NOT ok• The best way to improve site
search is– NOT to make new fancy algorithms– Instead … improve the interface
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The Philosophy
• Information architecture should be designed to integrate search throughout
• Search results should reflect the information architecture.
• This supports an interplay between navigation and search
• This supports the most common human search strategies.
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An Important Search Strategy
• Do a simple, general search– Gets results in the generally correct area
• Look around in the local space of those results
• If that space looks wrong, start over– Akin to Shneiderman’s overview + details
• Our approach supports this strategy– Integrate navigation with search
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Following Hyperlinks
• Works great when it is clear where to go next
• Frustrating when the desired directions are undetectable or unavailable
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An Analogy
text searchhypertext
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Main Idea
• Use metadata to show where to go next– More flexible than canned hyperlinks– Less complex than full search– Help users see and return to what
happened previously
Search Usability Design Goals
1. Strive for Consistency2. Provide Shortcuts3. Offer Informative Feedback4. Design for Closure5. Provide Simple Error Handling6. Permit Easy Reversal of Actions7. Support User Control8. Reduce Short-term Memory Load
From Shneiderman, Byrd, & Croft, Clarifying Search, DLIB Magazine, Jan 1997. www.dlib.org
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Information Architecture
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A Taxonomy of WebSites
low
low
high
high
Complexity of Applications
Complexity of Data
From: The (Short) Araneus Guide to Website development, by Mecca, et al, Proceedings of WebDB’99, http://www-rocq.inria.fr/~cluet/WEBDB/procwebdb99.html
Catalog Sites
Web-based Information
Systems
Web-Presence
Sites
Service-Oriented
Sites
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An Important IA Trend
• Generating web pages from databases• Implications:
– Web sites can adapt to user actions– Web sites can be instrumented
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Faceted Metadata
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Metadata: data about dataFacets: orthogonal categories
Query previews and navigation. Options to refine by course or season. Choose how you view the results
Searching within made all the difference. I could see how many results I was getting in each Very specific. I can choose more than 1 detail with search for recipe I'm looking for.Likes the way it narrows things down. And it gives you the numbers.
Found it simpler, more readable. Helped you hone in on the season.Liked the kid friendly, low fat optionWhy?
Can narrow down when you're stuck. You can always refine [your search].
Allowed me to make specific selections. I liked Browse too. Gave lots to choose from. Depends on what you’re looking for that day
Can limit and unlimit and limit again in a different way. Prioritize your criteria--change the first thing I clicked and go in a different direction. Easy to back up.
• People liked the browsing-style metadata-based search and found it helpful
• People sometimes preferred the metadata search when the task was more constrained – But zero results are frustrating– This can be alleviated with query previews
• People dis-prefer the standard simple search
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Missing From Epicurious
• How to scale?– Hierarchical facets– Larger collection
• How to integrate search?• How to allow expansion in addition
to refinement?
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Application to Image Search
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Current Approaches to Image Search• Visual Content and Cues, e.g.,
• QBIC (Flickner et al. ‘95)• Blobworld (Carson et al. ‘99)• Body Plans (Forsyth & Fleck ‘00)
– Color, texture, shape– Move through a similarity space
• Keyword based– Piction (Srihari ’91)– WebSeek (Smith and Jain ’97)– Google image search
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A Commonality Among Current Content-based Approaches:
Emphasis on similarityLittle work on analyzing
the search needs
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The Users
• Architects and City Planners
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The Collection
• ~40,000 images from the UCB architecture slide library
• The current database and interface is called SPIRO
• Very rich, faceted, hierarchical metadata
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Architects’ Image Use
• Common activitie:– Use images for inspiration
• Browsing during early stages of design
– Collage making, sketching, pinning up on walls– This is different than illustrating powerpoint
• Maintain sketchbooks & shoeboxes of images– Young professionals have ~500, older ~5k
• No formal organization scheme– None of 10 architects interviewed about their
image collections used indexes
• Do not like to use computers to find images
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Development Timeline• Needs assessment.
– Interviewed architects and conducted contextual inquiries.
• Lo-fi prototyping. – Showed paper prototype to 3 professional architects.
• Design / Study Round 2: – Developed 4 different detailed versions; evaluated with 11 architects;
results somewhat positive but many problems identified. Matrix emerged as a good idea.
• Metadata revision. – Compressed and simplified the metadata hierarchies
• Design / Study Round 3. – New version based on results of Round 2– Highly positive user response
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The Interface
• Nine hierarchical facets– Matrix– SingleTree
• Chess metaphor– Opening– Middlegame– Endgame
• Tightly Integrated Search• Expand as well as Refine• Intermediate pages for large categories
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Usability Study on Round 3• 19 participants
– Architecture/City Planning background
• Two versions of the interface– Tree (one hierarchical facet at a time)– Matrix (multiple hierarchical facets)
• Several tasks• Subjective responses
– All highly positive– Very strong desire to use the interface in
future– Will replace the current SPIRO interface
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Study Tasks1. High Constraint Search:
Find images with metadata assigned from 3 facets(e.g., exterior views of temples in Lebanon)
1.1) Start by using a Keyword Search 1.2) Start by Browsing (clicking a hyperlink) 1.3) Start by using method of choice
2. Low Constraint Search: Find a low-constraint set of images (metadata in one facet)
3. Specific Image Search: Given a photograph and no other info, find the same image in the collection
4. Browse for Images of Interest
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Interface Evaluation
• Users rated Matrix more highly for:– Usefulness for design work– Seeing relationships between images– Flexibility– Power
• On all except “find this image” task, users also rated the Matrix higher for:– Feeling “on track” during search– Feeling confident about having found all
relevant images
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Overall Preferences: Matrix vs. Tree
Simple search (e.g.
images of deserts)
Complex search (e.g.
exteriors of temples
in Lebanon)
Find images like this
one
OVERALL PREFERENC
E
Matrix 13 14 16 16
Tree 5 4 3 3
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User Comments - Matrix
• “Easier to pursue other queries from each individual page”
• “Powerful at limiting and expanding result sets. Easy to shift between searches.”
• “Keep better track of where I am located as well as possible places to go from there.”
• “Left margin menu made it easy to view other possible search queries, helped in trouble-shooting research problems.”
• “Interface was friendlier, easier, more helpful.”• “I understood the hierarchical relationships
better.”
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User Comments – Tree
• Pro– “Simple”– “More typical of other search engines I’d use”– “Visually simpler and more intuitive…Matrix a bit
overwhelming with choices.”
• Con– “I found SingleTree difficult to use when I had to
refine my search on a search topic which I was not familiar with. I found myself guessing.”
– “SingleTree required more thought to use and to find specific images.”
– “I do not trust my typng and spelling skills. I like having categories.”
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Task Completion Times
(Find Image is an artificial task: given a photo andno other info, find it in the collection.)
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When Given A Choice …
For each interface, one task allowed the user to start with either a keyword search or the hyperlinks.
3 chose to search in both interfaces
11 chose to browse in both interfaces
4 chose to search in Matrix, browse in Tree
1 chose to browse in Matrix, search in Tree
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Precision and Recall
Computed for tasks 1.1-1.3Pooling used for determining relevant setPrecision based on what was visible on screen
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Feature Usage Percentages
(Dark bars show subtotals)
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Feature Usage (%) Types of Actions
Action Categories
0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00%
Refine search (reduce# of results)
Expand search(increase # of results)
Arrange results
Start over/backup
Matrix
Tree
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Feature Usage (%) Refining
Use of Features to Refine Search
0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00%
Drill above images
Drill in matrix
Drill from image detail
Drill from large category
Drill by clicking "All N items"
Search within
Disambiguate keyword search
"More" in disambiguation
Matrix
Tree
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Feature Usage – Expanding / Starting Over
Use of Features to Expand Search / Start Over
0.00% 5.00% 10.00% 15.00% 20.00% 25.00%
Expand search usingbreadcrumbs
Expand by clicking X
Expand from imagedetail
Go back to start mid-search
Search all, mid-task
Back
Matrix
Tree
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Interface Evaluation
• Users rated Matrix more highly for:– Usefulness for design work– Seeing relationships between images– Flexibility– Power
• On all except “find this image” task, users also rated the Matrix higher for:– Feeling “on track” during search– Feeling confident about having found all
relevant images
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Application to Medline
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Summary and Conclusions
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Summary
• A new approach to web site search– Use hierarchical faceted metadata
dynamically, integrated with search
• Many difficult design decisions– Iterating and testing was key
• Conclusions:– Users like and are successful with the
dynamic faceted hierarchical metadata, especially for browsing tasks
– Very positive results, in contrast with studies on earlier iterations
– Note: it seems you have to care about the contents of the collection to like the interface
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Summary• We have addressed several interface
problems:– How to seamlessly integrate metadata
previews with search• Show search results in metadata context• “Disambiguate” search terms
– How to show hierarchical metadata from several facets
• The “matrix” view• Show one level of depth in the “matrix” view
– How to handle large metadata categories• Use intermediate pages
– How to support expanding as well as refining• Still working on it to some extent
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Advantages of the Approach
• Supports different search types– Highly constrained known-item
searches– Open-ended, browsing tasks – Can easily switch from one mode to
the other midstream– Can both expand and refine
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Advantages of the Approach
• Honors many of the most important usability design goals– User control– Provides context for results– Reduces short term memory load– Allows easy reversal of actions– Provides consistent view
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Advantages of the Approach
• Allows different people to add content without breaking things
• Can make use of standard technology
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Some Unanswered Questions
• How to integrate with relevance feedback (more like this)?– Would like to use blobworld-like
features
• How to incorporate user preferences and past behavior?