Given the interest in the search sessions at MW2008, and the follow-up discussion on Seb's, Nate's, and Brian's blogs, i though it would be useful to summarize a bit of the reading i've been doing lately for the phd + steve.museum research.

Directly relevant, is a longitudinal meta-study of the end-user search literature by Karen Markey, published in two parts in mid-2007 [in english, she compared and analysed the results of a lot of studies, conducted over a long time: 25 years].

The question of what makes a 'meaningful' number of queries of an on-line resource has been lingering in the back of my mind ever since i did the analysis of the Guggenheim Museum search logs last fall. At issue, really, is how to profile and analyse the long tail of user searching. (There was a D-Lib article about this not long ago, that talks about ways to analyse the nature of the tail.)

What brought this back to mind was a Hitwise Newsletter report that included the following analysis of terms that contain 'summer'.

Search Terms Analysis: Search Analysis- "summer" Search Term Analysis
Most popular keywords containing the term "summer" for the 4 weeks ending 05/19/07

i've recently taken a look at a year's worth of search log data from the Guggenheim Collection on-line -- a pilot study for some work within the steve.museum project. I've attached a draft paper to this post -- comments are welcome! It's still rough in spots, but I need to step back.

One of our premises in discussing folksonomy in the museum is that allowing users to tag collections will improve their retrivability... but surprisingly, we know almost nothing about what searchers of museum collections really do. i couldn't find a single serious IR study in the museum domain. There's lots of literature about what we 'should' do, how standards will help and why controlled vocabularly is really important, with almost no evidence to support those claims. We need to look hard at the data.

Notable findings in the Guggenheim data:

I've just been playing with another experimental image indexing tool, that's using image analysis to suggest keywords for images, called ALIPR: Automatic Linguistic Indexing of Pictures. You feed it an image file, or a URL to a web accessible image, and it suggests keywords that might apply to that image. You're then prompted to correct the suggestions, and add alteratives, so the program "learns" through user feedback and prompting.