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!
Thursday, 14 August 2008
Monday, 11 August 2008
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.
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.
Monday, 28 July 2008
The Tetris Model of Information Seeking
The more and more I've been reading about other models of information seeking (such as Marchionini 1995 and Kuhlthau 1993 and many more), the more I've been annoyed by how limited to a sequential flow they are. In Marchionini's, for example, there's a clear progression from problem identification, to specification, to seeking action, result viewing and resolving the problem. The model has this nice step towards the end that says 'refinement' and the text has a clause to say that people may drop back to almost any previous point. I believe a text clause like that is an indication that there should be a better way to model Information Seeking.
The thing I did like, was that each step was a different rectangular shape, based on how much time and computer involvement it required, as the two dimensions. These two observations about the model have led me to my tetris model of search, which I'm going to blog about here for a bit to test the water. You'll probably see followup blogs! I've got a lot to say about it!
Now, in Tetris, different shapes fall from the top of the screen, and success is modelled by organising them so that entire horizontal lines are made, removed from the display and converted to points. Let's first take the analogy that resolving an information seeking problem is like clearing a line of the board and that solving a bigger problem is like clearing multiple lines of the board, and finally that your score is representative of the overall knowledge you have on that topic.
Let us then imagine that the pieces that fall down from the top of the screen are then any one of the stages that are found in models like those mentioned above, where the ideal is that you get a series of simple pieces, representing a simple problem, a simple spec, a simple query, and a simple answer. BAM one line, problem solved.
BUT we all know that life is not like that, and regularly you get a nice simple first block (or you think you have a simple problem to solve) and then you get a + shape answer when you view the results that tells you your problem is a little more complicated than that. What we begin to see is that the complexity of a problem is actually represented not by the pieces, but by the current depth of the board. Each piece, therefore, represents an action, such as realising a problem, performing a query, etc.
So, a simple lookup on google is represented by a series of easy bits (specing, querying, viewing, etc) fitting together nicely and a line clearing. If you have a complex problem, however, the first bit you get is complex, like a +, and then you may need a combination of queries, and results to resolve your problem, and shift the 3 lines built by the +. Exploratory search can also be modelled with this analogy. If a user starts with a simple problem and starts off by querying for 'classical music' and then the first resullt says well there are lots of types of classical music:.... this means the next piece you got was a + and so getting an answer to your first query broadens the work you have to do to better understand classical music. Then, over time, you can resolve bits of information, find new problems you need to learn about. get some simple answers to fill in the gaps. Over time you may find that there are always rows with holes in, that you might take years to get back to them and fill them.
that was long, but think about it. I think its a pretty good analogy. Comments?
The thing I did like, was that each step was a different rectangular shape, based on how much time and computer involvement it required, as the two dimensions. These two observations about the model have led me to my tetris model of search, which I'm going to blog about here for a bit to test the water. You'll probably see followup blogs! I've got a lot to say about it!
Now, in Tetris, different shapes fall from the top of the screen, and success is modelled by organising them so that entire horizontal lines are made, removed from the display and converted to points. Let's first take the analogy that resolving an information seeking problem is like clearing a line of the board and that solving a bigger problem is like clearing multiple lines of the board, and finally that your score is representative of the overall knowledge you have on that topic.
Let us then imagine that the pieces that fall down from the top of the screen are then any one of the stages that are found in models like those mentioned above, where the ideal is that you get a series of simple pieces, representing a simple problem, a simple spec, a simple query, and a simple answer. BAM one line, problem solved.
BUT we all know that life is not like that, and regularly you get a nice simple first block (or you think you have a simple problem to solve) and then you get a + shape answer when you view the results that tells you your problem is a little more complicated than that. What we begin to see is that the complexity of a problem is actually represented not by the pieces, but by the current depth of the board. Each piece, therefore, represents an action, such as realising a problem, performing a query, etc.
So, a simple lookup on google is represented by a series of easy bits (specing, querying, viewing, etc) fitting together nicely and a line clearing. If you have a complex problem, however, the first bit you get is complex, like a +, and then you may need a combination of queries, and results to resolve your problem, and shift the 3 lines built by the +. Exploratory search can also be modelled with this analogy. If a user starts with a simple problem and starts off by querying for 'classical music' and then the first resullt says well there are lots of types of classical music:.... this means the next piece you got was a + and so getting an answer to your first query broadens the work you have to do to better understand classical music. Then, over time, you can resolve bits of information, find new problems you need to learn about. get some simple answers to fill in the gaps. Over time you may find that there are always rows with holes in, that you might take years to get back to them and fill them.
that was long, but think about it. I think its a pretty good analogy. Comments?
Friday, 25 July 2008
When should users be made to think?
I've been a little quiet, I know. I've been working hard consulting for an interesting new client on a project that has, yet again, completely consumed my interests with new challenges. In this case, its amazed me that one of the primary concerns of this project has not been to make the interaction as quick and simple as possible, but to produce software that is a) intuitive and b) coerces users into thinking about the appropriate things at the appropriate times.
I've noticed this has become a recurring theme in many scenarios. The first time I heard something along this line was an argument against automating the jobs of pilots, but for making the jobs easier. If pilots get used to the plane doing most of the jobs for them, they may become less capable when the plane malfunctions. However, if the actions are made easier to perform, then the skill is maintained, but the usability has improved.
Search can be considered in a similar way. While lots of search designs are focused on letting users express the knowledge or known constraints that they do have quickly, this can leave users with problems when they have to choose between their results using facets that they have not considered. In this case, we do not at all want to make assumptions, but at the same time, we do not want to leave them to make a decision with only a list of options to do so with.
In some of our previous work we have investigated how giving users example (ideally multimedia) result items that would be associated with each of the items in their new decision. This means that users are become aware of things that the should think about before they buy, AND give them the means to understand the effect of their decisions. Another approach mSpace has taken is to be subjunctive, by allowing users to easily change their mind, or considered another way: rapidly tryout different options by minimising the costs of reversing their decision. To do this mSpace maintains all of the options a user was given at each step, so that the user, with a single click, can switch between different items in the same facet, and see the effect is has on the results.
I've noticed this has become a recurring theme in many scenarios. The first time I heard something along this line was an argument against automating the jobs of pilots, but for making the jobs easier. If pilots get used to the plane doing most of the jobs for them, they may become less capable when the plane malfunctions. However, if the actions are made easier to perform, then the skill is maintained, but the usability has improved.
Search can be considered in a similar way. While lots of search designs are focused on letting users express the knowledge or known constraints that they do have quickly, this can leave users with problems when they have to choose between their results using facets that they have not considered. In this case, we do not at all want to make assumptions, but at the same time, we do not want to leave them to make a decision with only a list of options to do so with.
In some of our previous work we have investigated how giving users example (ideally multimedia) result items that would be associated with each of the items in their new decision. This means that users are become aware of things that the should think about before they buy, AND give them the means to understand the effect of their decisions. Another approach mSpace has taken is to be subjunctive, by allowing users to easily change their mind, or considered another way: rapidly tryout different options by minimising the costs of reversing their decision. To do this mSpace maintains all of the options a user was given at each step, so that the user, with a single click, can switch between different items in the same facet, and see the effect is has on the results.
Wednesday, 16 July 2008
viacom aint all bad - perhaps i love viacom?
I've been a bit quiet recently, having been travelling to institutions around the UK, and then vacationing a bit. But i was pleased to see the news on the row between YouTube and Viacom. Thankfully they have agreed to let Google anonymise the usage histories that will be sent to Viacom as part of the ongoing copyright legal battle.
This only leads me to think that Viacom has created an even more perfect dataset for us to run user analysis over, like the longditudinal study we recently presented at JCDL2008 this year. I assume each user will get an anonymous ID, so that anonymous individual activity can be followed?
Can we have it after you Viacom? We'd love you!
This only leads me to think that Viacom has created an even more perfect dataset for us to run user analysis over, like the longditudinal study we recently presented at JCDL2008 this year. I assume each user will get an anonymous ID, so that anonymous individual activity can be followed?
Can we have it after you Viacom? We'd love you!
Saturday, 5 July 2008
the world is connected
actually, from the map it looks like the 1st world is connected at least.
interesting visualisation, though, of twitter conversations.
compared to some of the other network visualisations, this one shows how being grounded in a known 2.5D space makes it a lot more accessible.
Friday, 4 July 2008
best search dataset ever?
In a current lawsuit between Google's YouTube and Viacom, Google have been instructed to hand over their logs of user viewing habits. Google are upset about it, but at least the judge overruled the request for Google to hand over their source code for filtering copyrighted material! Google have requested, although its not been confirmed yet I believe, that they get to anonymise the logs first, to respect the users' privacy. i personally hope this is allowed.
Anyway, despite this interesting privacy issue, i can't help but thinking that 12 terabytes of usage logs from youtube would be an AMAZING research resource for investigating user behaviour. Sadly they dont have facets to chat about, but it could tell us how people have used query refinements, spelling corrections, categories, filters, similar clips, recommended clips and so much more!
Google might as well do something useful with it, if the data is going to shown to at least one third party.
Anyway, despite this interesting privacy issue, i can't help but thinking that 12 terabytes of usage logs from youtube would be an AMAZING research resource for investigating user behaviour. Sadly they dont have facets to chat about, but it could tell us how people have used query refinements, spelling corrections, categories, filters, similar clips, recommended clips and so much more!
Google might as well do something useful with it, if the data is going to shown to at least one third party.
Subscribe to:
Posts (Atom)
