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Uzanto
Digital categorization
Stage 1Multiple
concepts are activated
Stage 2Choose ONE of
the activated concepts.
Categorize it!
Note the chosen concept
Stage 0Object worth remembering
(article, image…)
Cognitive process behind categorization
Analysis-Paralysis!
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Tagging is simpler
Stage 1Multiple
concepts are activated
Tag i t!Write down all
activated concepts
Stage 0Object worth remembering
(article, image…)
Cognitive process behind tagging
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Tagging: Input versus Output
Output Structure
Input EffortLow High
Low
High
Tag Clouds
Hierarchicalcategorization
Facets
Clusters
Collaborative Filtering
If we could get to Facets / clusters from tags, it would be best of both worlds
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Getting at structure through tags: (a) Card Sorting
http://www.rashmisinha.com/archives/05_02/tag-sorting.html
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Getting at structure: (b) Collaborative Filtering
• Challenges with Input: Getting lazy users to provide input (some indication of interest) Implicit (infer user intention from behavior). e.g., past
purchasing behavior on Amazon Explicit (powerful, difficult to get people to rate stuff) e.g.,
ratings on Pandora, MediaUnbound)
• Challenges with interface Hard to convey what system is doing (no direct cause &
effect as with search) Need to make system logic transparent (e.g., Amazon)
• Users who liked X, also liked Y• Users who searched for X, also liked Y• Users who bought X, also bought Y
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Varieties of Collaborative Filtering with Tags• Tags as an indicator of interest
Tagged url as input, url as output
People who like this url also like these other urls Tagged
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Using tags as semantic information (with CF algorithms)• Tags as input: Tags as output
(People who use similar tags also use)
• Tagged urls as input: Tagged urls as output People who tagged similar urls’ also tagged these urls
• Tag-url conjunction as input: Tagged url’s as output People who tagged similar url’s with similar tags, also
tagged other url’s with same tags
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User Experience in such Search/Browse interfaces
•More of a controlled experience
•Every movement (forward, making a turn, backwards) is a conscious choice. (need information at every step)
•User might make mistakes, and retract (go back) a step or two or start again. Each of these is a conscious choice.
Like being in control of a car
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User Experience with Recommender Systems
•-user has less control over specifics of the interaction.
•System does not provide information about specifics of action
•more of the black box model (some input from user, output from systems).
Like riding a roller-coaster
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Tags & Clusters
This idea is not new!
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Clusters on Flickr
• Positives: No effort to name the clusters (makes clusters easy to understand) No fancy visual ways to show the clusters, simple text and image
based Clustering tags, rather than items (computationally easier, easier
to understand( No Other or Misc category
• Negatives Hard to understand the overall space and the individual
categories Much harder to do with anything but images
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Tags & Facets (the holy grail?)
• You have several orthogonal classifications. For example, Wine.com
Wine can be described by type, region, price, vintage.
Instead of putting all this in one large hierarchy, you can have several hierarchies
Natural afffinity between tags and facets• With tags you don’t need to know where stuff lives,
just know a few of its descriptors• You can have as many facets as you want• There are multiple ways to access a piece of
content
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From tags to Facets
• More than one way Compute facets from tags in some intelligent manner
RawSugar• Uses tags• Some sort of a taxonomy at the back end to make
sense of tags
http://www.rawsugar.com/collections/oferben
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Chandler approach
• People tag mail and other items
• Can promote tags to facets
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