Freebase Parallax

The video of the new Parallax interface for Freebase, recently distributed by David Huynh, is brilliant. I totally recommend you watch it. A fellow exploratory search fanatic, Daniel Tunkelang, has blogged about its qualities in terms of linking between different entity types, which is quite special.

What I especially love about is the simple clear layout metaphor that it uses. It's a very clean left-to-right system (although this, i suppose, is more familiar for Western countries). It works a little like this: Your current results are smack bam in the middle infront of you. Anything to the left is something 'before' and anything to the right is something 'after'. The facets on the left are ones that apply to the current type of object in the results list. So if you select an item from any of the facets to the left (which are 'before' your current results) your results are reduced/filtered. The example in the video is that you are looking at a list of presidents, and by selecting a political party on the left, the presidents are filtered.

The facets on the right, however, are different types of related objects that you could move 'forward' to from your current data. The example they give is to the presidents' children. This moves you forward to see a list of people who were the children of (the filtered set of) presidents. They have different (or though some will overlap (like gender)) facets on the left which can be used to further reduce the children shown. The facets on the right are new objects that you can move 'foward' to from children.

All the while the left to right progress is mirrored by a left-to-right breadcrumb above the current information so you can see the steps 'forward' you took.

its all very nice, clean, clear, and still lets you browse endlessly through interconnected heterogenous data types. Nothing less that you expect from David Huynh!

can faceted search overload users?

For a while now, its been a concern for mSpace that we have been classified as good for intermedia/expert users, when lots of our work has focussed on making searching easier for people. We showed that the spatial layout was good for elderly people whose working memory was less capable at remembering things as other brosers change their layouts over time. Having said that, we too have watched participants of userstudies experience a moment when they first see the interface, with not knowing where to start.

An approach taken by other faceted browsers, such as the one provided by Endeca, is to change the layout a lot, but by taking away the decisions that people have made, so that all they have to do is look at the most important factors remaining. It's the same policy that google have - keep the options and ui as simple and clear as possible.

I've been seeking a way of finding out once and for all if there is a measurable aspect of UIs that we can use to prove that one way is better than the other. or that there is no difference at all. Certianly in mSpace we have shown that any overload experienced at first soon expire, and users have a real rich experience during search. Can mSpace be redesigned slightly to remove this initial wall and still give them the added richness of interaction that we have been striving to improve over the years.

Excitedly, i read a paper on cognitive load theory this morning on the train, which talks about what aspects of computer interfaces might make it harder for people to find and learn from information. I think this may hold the answer I have been looking for. It has measures and terms within the cognitive load theory for the effect caused by having duplicate, or redundant, or combined sources of information, on peoples ability to clearly and easily use a UI. I'm going to try running some numbers to see if any significant differences in the approaches taken to faceted browsing that might reveal why mspace is deemed intermediate/expert, and the approaches used by websites like walmart and diy.com are deemed simple for novices.

the best way to provide faceted search?

As research has produced an increasing number of insights into the different ways of providing faceted metadata to users in the form of a faceted browser, the question has become: what actually is the best way to provide faceted search? This same question has not really been seen in typical information retrieval, as each bit of research has (usually) incrementally improved the system performance, and a good keyword-based search system will try to include all the advancements in their UI (not that Google is providing interactive query refinements).

We actually do see faceted search all over the place. iTunes has it in their 'browser' function (3 columns that filter to the right). Google product search lets you refine by facets like brand and price (each facet filters every other facet). Endeca seem to be selling it to everyone these days (right on!), including Walmart and Borders.

There are actually 2 layers to this question: how best to provide a faceted classification and how best to provide a faceted browser. The earlier has been well investigated, with advice from Marti Hearst. Endeca certainly seem to ask 'after each click in a facet, what is the best set of facets and values to show the user?'. The second question is less well known. Even one of Endeca's clients, the NCSU library, are asking: should we have the facets on the left or the right?, should we place a breadcrumb or a list of decisions?. How does this affect the user?

Further to these layout questions, I have been trying to work out for a while now whether the structured and consistent iTunes approach is better or worse than the dynamic adaptive approach taken by Endeca? Especially with all the additional functionality (e.g. column swapping and backward highlighting) we have been adding to the iTunes-style approach with mSpace. There are even more additional questions to ask. maybe its a case of when is one better than the other? Finally, can we somehow take the best of both worlds, so that we can figure out what to add to our faceted browsers that make them incrementally strong.

first faceted system?

Daniel Tunkelang, chief scientist at Endeca, has passed on an excellent entry on perhaps the first faceted system. It's actually come at a very timely point during the JCDL08 conference, where faceted browsing has been quite core to a number of discussions. I'll post more about this later. Someone even asked me about Ranganathan's colon classification (research core to the start of faceted classifications) after my talk on a longitudinal study of faceted and keyword use, and now i have link to research it further - thanks Daniel.