As for the amount of information associated with the information need, I’d say the issue is orthogonal in theory, but not in practice. In theory, looking for sets (or set-based analyses) is just like looking for documents, at least with respect to the simple exploratory search dichotomy / spectrum I’m describing. In practice, however, looking for a set tends to be more complicated than looking for a single document or information snippet. In particular, you almost never know if you’ve found everything.

Another thought: Does it matter to your model how much information is required to satisfy the info need? E.g. does it matter whether the user is looking for a single, existing piece of information vs. a set (synthesis, summary, contrast-ment) of information?

Is that issue completely orthogonal to your two dimensions, or is it related?

Is exploratory search like NP?

http://irgupf.com/2009/11/19/google-is-to-exploratory-search-as-p-is-to-np/

@Daniel, “But I concede that the available data for images on the open web makes such a textual interface a formidable challenge. I’ll certainly be the first to cheer any steps toward overcoming it.”

Grrrrr! Hopefully by the tail-end of the year. Just too busy writing other search services to get the new image-search demo online using flickr images.

http://www.ischool.utexas.edu/~i385t-sw/readings/Marchionini-2006-Exploratory_Search.pdf

what is the purpose of it.

So, sure, a fun toy, but not more than a toy until the relevance is much higher and the clusters either intuitive or explained.

I agree, exploratory search requires explanatory search.

BTW, Greg, to tie this back to an earlier discussion: That’s one of the ongoing questions I have about using A/B testing in a web environment. Suppose you launch a new feature, algorithm, or interaction design. You split your users into buckets, and see if there is enough uptake on the new bit. However, if the users don’t have a clear model of what it is you’re trying to do for them, how the tool is meant to work and when and why, how do you really know that they aren’t just using the new tool (or interaction mechanism or whatever) to do things in their old way, the way in which they have become accustomed? Especially since whenever I’ve been bucket tested (and I think I have noticed a couple of times when I’ve been a test subject) the new bit has never come with any sort of explanation as to what is going on. It’s just suddenly different.

I suppose that there is nothing intrinsic to A/B testing that says a new system can’t come with a bit of text, overlay, whatever, that tells the user a little bit about it, how it should be used, what it’s trying to do. Nevertheless, it rarely if ever does include such things.

Why is that?

If the purpose is exploratory, then the criteria for the clusters needs to be intuitive and obvious. In my usage, clusters usually had nearly identical images, occasionally visually similar images, and often apparently unrelated images. Links between clusters seemed useful and understandable only a slim majority of the time.

So, sure, a fun toy, but not more than a toy until the relevance is much higher and the clusters either intuitive or explained.

Maybe someone knows but isn’t this image searching by (cleaned up) image labels/tags.

For example, if you click on ‘cup’ on the far right list ie. http://image-swirl.googlelabs.com/html?query=cup# you get http://image-swirl.googlelabs.com/html?query=cup#.

Two types of cups are being differentiated – cups for drinking and cups as in sports trophies which is good but it doesn’t seem satisfactory. A case of the cup hasn’t runneth over!

All too often, the realities of data can work against this. On many catalog sites supporting query refinement by color, for example, the stock images available are not necessarily in the chosen color. So, refining to “red” products will often show pictures of black ones, but the fact that they are available in red not immediately apparent (worse, of course , is when the reference to “red” is only incidental in their description).

Image Swirl looks like it has a way to go to get over the threshold of user comprehension. My sample queries produced many very similar pictures. What was the difference between one group or another? I couldn’t tell, so I couldn’t understand how the mechanism worked. I couldn’t decide how to move forward, and I wasn’t sure what would be different from clicking one image group versus another.

To make this work, it really needs to be apparent to the user what the choices are. Is it clear that all these pictures are together because they’re night scenes? Or because it’s the same building? Or because there’s a particular face? Etc.

That said, what’s being attempted here is an immensely hard problem, and I applaud the effort.