Guided Exploration = Faceted Search, Backwards by Daniel Tunkelang.
Daniel starts off:
Information Scent
In the early 1990s, PARC researchers Peter Pirolli and Stuart Card developed the theory of information scent (more generally, information foraging) to evaluate user interfaces in terms of how well users can predict which paths will lead them to useful information. Like many HCIR researchers and practitioners, I’ve found this model to be a useful way to think about interactive information seeking systems.
Specifically, faceted search is an exemplary application of the theory of information scent. Faceted search allows users to express an information need as a keyword search, providing them with a series of opportunities to improve the precision of the initial result set by restricting it to results associated with particular facet values.
For example, if I’m looking for folks to hire for my team, I can start my search on LinkedIn with the keywords [information retrieval], restrict my results to Location: San Francisco Bay Area, and then further restrict to School: CMU.
But quickly comes to:
Guided exploration exchanges the roles of precision and recall. Faceted search starts with high recall and helps users increase precision while preserving as much recall as possible. In contrast, guided exploration starts with high precision and helps users increase recall while preserving as much precision as possible.
That sounds great in theory, but how can we implement guided exploration in practice?
A very interesting look at how to expand a result set and maintain precision at the same time.
Of particular interest for anyone who wants to implement dynamic merging of proxies based on subject similarity.
An open field of research that offers a number of exciting possibilities.