trendpreneur

In the red corner today, a startup that caused a bit of buzz at the Web 2.0 Expo.

Plista’s presentations grabbed my interest from the start, since Dominik (speaking, above) outlined the information overload problem in stylish detail. Of course, it’s a pretty big problem and there are plenty of ways to solve it — Plista focuses on the recommendations approach.

The key components seem to be collaborative filtering and use of social graphs to build up a recommendations system that works across different sites; this allows for some interesting user tracking and the creation of central information profiles that can then be tailored to different output. A really interesting idea was the preference Doppelganger, someone who likes the same stuff you like — this is something I’ve vaguely touched on in the past.

While someone might like the same things as me, so I can use their behaviour to predict my own, I’m actually quite interested in the social graphs around that person and how they resemble mine; how the influence web bends around them compared to mine; and how that person’s key information sources differ from mine (are they reading a blog I’m not?). I guess this is more meta-informational than based in product recommendations, but there’s such a huge potential for discovery here. On the other hand, we once again run into privacy issues. My preference profile is going to be very diverse, do I really want people interested in the same technologies as me told which bands I like? To what extent are we really similar? And how do the different preference dimensions interact with each other? (Maybe people who use Open Source software prefer organic, eco-friendly products. Maybe they don’t.)

But back to Plista. An engaging presentation and a product that looks interesting, especially due to its cross-domain, supra-web type nature. An information overload company worth watching, even if it isn’t solving the problem in the same way I am.

(A bit more about Plista at CNet and Crunchbase.)

Encouraging users to ‘thumbs up’ or ‘thumbs down’ items is a great way to get some sentiment-based feedback on what can be an unmanageably large amount of data. But how reliable is it?

Both FriendFeed and Socialmedian have a binary way of saying you found a particular news item or post interesting – a quiet nod of approval, if you will. I like this. I don’t like this. As commenters have pointed out, the word ‘like’ isn’t always appropriate (I “like” the story about a celebrity suicide?) but that’s purely semantics.

What’s the point, though? By ‘liking’ items on FriendFeed you can help populate ‘best of’ lists, and aid uses in seeing at a glance what’s worth looking at. On the other hand, why do I care if Joe Bloggs, friend of Robert Scoble, likes an item? He might find entirely different things interesting to me. When I only know one of the people who likes a story, is there real value in pulling out ‘most liked’ items?

[...]