Here is a not so original and revolutionary idea – have services use the information they gather about you, your habits, and your interests to target content you will be likely interested in. This has made Google’s founders very rich and is at the core of many established companies and emerging startups. With the amount of content produced daily, many services come up with their own way of making some content stand out. From manual editorials like the New York Times, to community driven efforts such as Digg, different sites help you find the content you want faster and with less noise.
But how do you apply this to microblogging? In a perfect world, the more people I choose to follow or subscribe to, the less content I expect to get. Why? Because the system should be able to figure out
what it is I am after and filter out the updates that are not part of the pattern. Of course we need some grouping capabilities and creating multiple streams for our multiple interests, but the idea still stands.
For example, if I followed Joe’s Twitter stream after reading a tweet about a cool restaurant, it might be a hint as to my interest in Joe’s content. If I subscribe to three people all with common keywords, it is likely I am more interested in that then some of their more random thoughts.
Yes, some content will always get lost but that is true with any filtered stream, and let’s face it, most working folks cannot keep up with the amount of content produced even in our very narrow field. Instead of following Scoble’s tweets, I read Valleywag – after all, they get paid to read it and let me know when something funny shows up.
Netflix seems to get it right – the more films you rate, the less options it gives you for other films you might like. They use your data to better fine tune what you are after. They don’t just offer you 10 new movies for each one you just rated. It is an approach that encourages more user interactions, not less as in constantly having to dump your Twitter friends.