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Collaborate or Die

Published: May 28, 2007

Author: David Rodnitzky

Lately I’ve been playing around with StumbleUpon.com and it’s pretty addictive. For those of you not familiar with this site, it works like this: you download a toolbar for your browser that says “Stumble.” When you click on it, it takes you to a site that it thinks you might like. This is determined by the categories you select as areas of interest, and also by how you rate past sites you have visited.

For example, let’s say that StumbleUpon shows me PayScale.com, an online compensation survey. I rate it as a “I like it.” The site has also been rated by many other users as well. Over time – after I’ve rated maybe 50 to 100 sites – StumbleUpon can match me up with other users that have also rated similar sites similarly. As a result, it can start showing me sites that people like me have already reviewed, making it likely that I will also like these sites. This is known as “collaborative filtering”, most famously seen in Amazon’s “people who liked this, also liked this” box.

Right now, StumbleUpon is basically a fun way to find funny or cool sites. I mostly end up with joke sites, strange pictures, or YouTube videos. The potential of StumbleUpon, however, is much greater. Indeed, rumor has it that eBay is about to acquire the company for $75 million. That’s a lot of dough for a service that sends people to lawyer joke summaries.

But think about StumbleUpon this way: if I rate sites for a few months, I’ll probably have 500 to 1000 sites rated. That’s a pretty good profile of my likes and dislikes. Now let’s say that the Stumble toolbar expands from just the ability to randomly access cool sites to an actual search box. So now I type in “los angeles airplane tickets” and StumbleUpon determines that people like me really prefer Kayak.com over Orbitz. As a result, I get sent to the result that works best for me. Sort of like Google’s “I’m feeling lucky” feature, but much more personalized.

You could even take a regular search engine approach and show me the top ten listings based on my personalized preferences. If you think about it, which would you prefer: results based on sophisticated computers with oodles of complex algorithmic code, or results based on 500 people just like you who have already looked through a bunch of sites and found the two or three that they like the most. Personally, I’d bet on the people.

As much as a lot of Web 2.0 companies seem to be over-hyped, and over-valued, I do think that companies that use the “wisdom of crowds” to personalize results have the potential to provide far more accurate results for users than an algorithm by itself. I count Flickr (photos), Digg (news stories), and StumbleUpon (Web search) as the leaders in this arena.

Granted, as with any technology, the more popular it becomes, the more likely it is susceptible to manipulation. Just as the rise of search engines created an entire industry of “search engine optimizers”, so too will the rise of “social media” create an entire industry of “social media optimizers.”

It may, however, end up being harder to game social media than it is to game a search engine. I can envision closed networks of users that work as a collective to decide the best sites for their group. In other words, in the current social media world, it is possible to “Digg bomb” and generate buzz around a news story simply by spamming the results and voting a site up the ranks. But if users have the ability to approve or reject members of their ‘crowd’, you could truly end up with a spam-free world where you really trust the results that you get back from the social media engine.

The number of people using StumbleUpon, Flickr, and Digg is still very small – probably 90% live within 50 miles of San Francisco! But once these sites reach critical mass, you have to wonder whether traditional search sites like Google and Yahoo will start to seem far less relevant and somewhat anachronistic.

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