How’s discovery different from search?
Guest Author Pete Warden
A lot of the really interesting services out there are about discovery, rather than search. Since they’re both ways of finding content, it’s worth looking at what makes them different.
Search is goal-oriented, discovery is about the journey. It’s the difference between going to the hardware store to get a Phillips screwdriver, and browsing the travel section in a book store. Rather than having very specific criteria in mind for what you want, you’re using indirect clues to help you find something that will meet your general needs. For a travel book that might include whether you’ve seen it mentioned in a review, if you’ve enjoyed the author’s work before, if it has an attractive cover, if there’s praise from people you trust, if your hairdresser mentioned the location, if you’d seen a documentary on the area, or if it happened to be sticking out from the shelf a little more than the others.
Search is solitary, discovery is social. Most of the factors behind buying a travel book are about interactions you’ve had with other people. Digg is one example of trying to emulate some of those traditional mechanisms for finding popular items. Facebook’s addictiveness is all about being tapped into the pulse of your social circle, not with a particular goal in mind, but just to keep up with the context and doing the equivalent of picking fleas off each other’s backs (now that’s a Facebook App idea!). The power of me.dium comes from the injection of social context into browsing. It restores the cues we’re used to in the physical world, so we can judge locations by seeing where both our friends and strangers hang out.
Search is about universal answers, discovery is customized. Though Google talks about searching the sites your friends frequent, I think that functionality will be much more useful for discovery. It’s not likely that your social circle will be visiting the most authoritative sites on all the specific questions you’ll want to get definitive answers on, the sample size will just be too small. Instead, finding out which sites on general topics are popular amongst your circle would be a lot more interesting. Discovery is a lot more about your personal taste, and that’s something you likely share with your friends a lot more than the general population.
Discovery is a background task. Often you’ve got some general interests that you want to know more about, but you’re not actively taking steps to find out more. Instead you’re keeping an ear to the ground while you get on with other activities. This is where applying some social network techniques to the workplace can be really interesting. Seeing updates on what your colleagues are up to will often trigger some thoughts or connections on topics you’re interested in, and lead to discussions you wouldn’t have had otherwise. That could be the real killer app for social networks in the enterprise.
February 23rd, 2008 at 2:38 pm
Well said, thanks for posting.
Cheers
Sahar
February 25th, 2008 at 12:03 pm
Great post. Collarity’s approach to discovery leverages the collective intelligence of a website’s natural communities-of-interest through their anonymous behavior — helping all the other web visitors find the content they need from the site.
Real Life Example: You’re in a room with 1,000 people and you have a question regarding the best way to file your taxes. You don’t necessarily want to get the opinions from of all 1,000 people people in the room. You also may not want to get answers from your friends (who may know little about taxes) or even the people most willing to answer your question (potentially tax zealots). What you want is to find the cluster of tax experts in the room and get help from them.
That’s essentially what Collarity does for web publishers. We create implicit attention communities, centered around the natural activity-driven interest/subject areas of the site — we find the tax experts in the room (site). So now, when someone begins searching/browsing for tax filing information, recommendations and search results are filtered/ranked by the “tax community” through their implicit anonymous behavior.