Back in 2002 I was involved in the building of a knowledge model for drug discovery, intended to be used by a knowledge portal of a major pharmaceutical group. Not sure it ever was implemented, but the work was great food for thought. Asking a leading scientist there what were his main functional requirements for a knowledge portal, I was stunned by the obvious simplicity of his answer. In short:
I want the system to stop pushing to me things I already know, such as my own publications, or those of my students and colleagues. What is of interest to me lies just behind this, one click away over the edge of my current knowledge. What I want to be pushed to me by the system should be different enough to question my current knowledge and make it move forward, but close enough to be easily connected to it.
I've met this requirement over and over since, made more or less explicit by all kinds of users. In a nutshell the interesting knowledge is both close to mine and different. It's the stranger living next door. But actually I've not seen yet any application meeting this requirement.
Indeed many applications push stuff based on user profile, social recommendations etc. But most of the time what they push to the user is something (or someone, in the case of social network recommendations) possibly unknown, but close and similar. The basic mechanism is Amazon's "if you like this, you should probably like that", or LinkedIn's "meet a friend of your friends". Very often the recommended stuff or person is not that unknown, and when it is, most of the time it's just adding a layer to your current knowledge or social cocoon. To find out something or someone both new and challenging, the best way is still to-date random browsing and serendipity. That's basically how I found out about PDF 2010 conference, through an excellent report by Marcia Stepanek and Ethan Zuckerman's post about Eli Pariser and Filter Bubbles, both providing excellent background reading for what I'm pushing here.
But how does one spot the stranger next door? Well, she's somehow different. Maybe the emergent social-semantic web tools can help to find out this. Imagine an interface where users would pick data and people making together a comfort zone representative of their current knowledge and network. First the system would check if this choice is globally consistent, and if yes search the edge of this comfort zone by any convenient follow-your-nose algorithms, and discover assertions related to, but not consistent with the user's current view of the world. So instead of like-minded folks and similar readings comforting my knowledge cocoon, I would see popping up on my dashboard "John Bar, which you might know, has a different view about topic Foo. Do you want to discuss this now?", along with a cool visualization based on the inconsistent triples.
Now that would be an exciting way to explore the social-semantic edges, avoiding the pitfalls of both cocooning and random serendipity. Did you say killer app?