Digital categorization Stage 1 Multiple concepts are activated Stage 2 Choose ONE of the activated concepts. Categorize it! Note the chosen concept Stage 0 Object worth remembering (article, image…) Cognitive process behind categorization Analysis-Paralysis!
Tagging is simpler Stage 1 Multiple concepts are activated Tag it! Write down all activated concepts Stage 0 Object worth remembering (article, image…) Cognitive process behind tagging
Tagging: Input versus Output Output Structure Input Effort Low High Low High Tag Clouds Hierarchical categorization Facets Clusters Collaborative Filtering If we could get to Facets / clusters from tags, it would be best of both worlds
Getting at structure through tags: (a) Card Sorting http://www.rashmisinha.com/archives/05_02/tag-sorting.html
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
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
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
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
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
Tags & Clusters This idea is not new!
Clusters on Flickr
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
Hard to understand the overall space and the individual categories
Much harder to do with anything but images
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
From tags to Facets
More than one way
Compute facets from tags in some intelligent manner
Some sort of a taxonomy at the back end to make sense of tags