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FaceTag Integrating Bottom-up and Top-down Classification in a Social Tagging System IA Summit 2007 22-26 March, Las Vegas
The Long Tail Our culture and economy is increasingly shifting away from a focus on a relatively small number of "hits" at the head of the demand curve and toward a huge number of niches in the tail
These problems can dramatically reduce the effectiveness of the application and the benefits brought on by tagging systems.
User Experience Issues
Low findability quotient and Low scalability
High semantic density (very few well-known topics dominating the scene)
An alphabetical criterion limits the ability to explore the tag cloud
A flat tag cloud cannot visually support semantic relationships
Tag clouds are visual interfaces for information retrieval that provide a global contextual view of tags assigned to resources in the system
Flat tag clouds not sufficient to provide a semantic and multidimensional browsing experience
Navigating Large Domains
Information seekers in large domains of objects express the desire of having to deal with meaningful groupings of related items, in order to quickly understand relationships and decide how to proceed [Marti Hearst 2006].
How to generate and navigate such groups from a flat set of objects is anyway a different matter.
Taxonomies, Clustering and Faceted Classification have been proposed in the past as useful techniques
Taxonomies = coherent and complete system of meaningful labels which systematically organize a domain
Organically crafted before starting to catalogue trying to guess user needs and content types
Authoritative centralized view
High precision avoiding ambiguity, hierarchical structure to give context
An a priori and monolithic hierarchical organization do not have the ability to match the vocabulary and the varied ways of thinking of different users.
Expensive to build and maintain by professional indexers
Clustering = the act of grouping items according to some measure of similarity
It reduces the semantic density and improve the visual consistency of tag clouds
But it generate messy groups , conflate many different dimensions and does not allow refinement and follow-up queries
Users prefer clear hierarchies with categories at uniform levels of granularity over the messy, unpredictable and unlabeled groupings typical of clustering techniques
Facets = orthogonal descriptors (categories) within a metadata system
Each facet has a name and addresses a different conceptual dimension or feature type relevant to the collection
Each object is classified combining labels from different facets.
Facets can be :
Flat or hierarchical
Assigned single or multiple values
It is hierarchial Facet name 393 resources here
add structure and context to tags
navigate along several dimensions simultaneously
seamlessly integrate browsing and searching
refine and broaden filtering criteria
Hierarchical faceted metadata can be used to
Better support for exploration , discovery and iterative query refinement.
Easier to understand the meaning of tags
Large tag clouds more browsable
Reduction of the mental work (favoring recognition over recall)
Usability studies show how this approach is preferred over single hierarchies and clusters
What do we need next?
What do we need yet?
Middle ground (between the pure democracy of bottom-up tagging and the empirical determinism of top-down controlled vocabularies*)
Metadata ecology : merge and leverage emerging and traditional tools to improve findability
Metadata ecology as a fusion not only a coexistence
*Alex Wright: http://www.agwright.com/ blog / archives /000900.html
FaceTag tries to limit the impact of polysemy, homonymy and basic level variation introducing a multidimensional and more semantic paradigm
Goals : improving usability, findability, browsability, serendipity and scalability of the system.
FaceTag mixes three contributions to social tagging systems:
Facets of Tags
Tagging and searching mixed
How to choice facets? 2 main roads
Each facet is created at the moment
The subject is freely deconstructed in several aspects
Each facet is chosen using the guidelines fixed by Ranganathan or CRG
The subject is deconstructed following a “general scheme”
“ General scheme” works as a prototype of every particular faceted scheme
Facets derived from the CRG general scheme Date Time [Country] Space People Agent Purposes, [Markets] (e.g. Industry, Health ...) Patient -- By-product Deliverables Product [Activities] Operation -- Process Themes Material Language Property -- Part Resource Types (e.g. online report, case study, blog...) Type [Documents, Resources] Thing FACETAG CRG
FaceTag facets date of publication (through a calendar) Date public administration health, education > conferences > www2006 Purposes Weinberger e. Reiss Morville People competitive analysis classification > facets web 2.0 > folksonomies information design > navigation design > breadcrumbs Themes predefined values (select box ISO Standard ISO 639-2) Language white paper case study blog > enterprise web Resource Types SAMPLES FACETAG FACET
The Real Application
FaceTag Home SEARCH + TAG SUGGESTION FACETS + FACETED TAGS RESULT SET
Search Term Selected SEARCH TERM UPDATED TAGS UPDATED RESULT SET
Search Term + Tag Selection FACET TAGS for this FACET FACETED BREADCRUMBS UPDATED RESULT SET
Final Step UPDATED TAGS RESULT SET
Input Form WYSIWYG EDITOR FACETS TITLE and URL HIERARCHICAL TAG SUGGESTION
Preliminary User Research
Preliminary facets were derived from the CRG schema
These facets will be revised through an iterative bottom-up (card sorting) process
The goal: to elicit the best facets from a wide set of IA bookmarks already online
An intuitive, easy to learn and easy to use interface is probably the single most effective way to support and stimulate participation
The new user interface has been designed through documented heuristics and patterns
Verified at each iterative step by small usability tests .
Critical task: the assignment of new bookmarks and the association of tags to relevant facets.
User Interface Evaluation
Folksonomies are dead. Long Life to folksonomies
Low Benefit & Mainstream Ready?
28% of online Americans tag content (PEW December 2006)
Tagging is commoditized, but it has a long way to go
A few ideas with FaceTag
Hierarchies Facets Tag Suggestion Hierarchical Clustering Tagging Decay Advanced Navigation Synonyms Syntax Control User Ratings