A social filtering algorithm

A social filtering algorithm

This post is not directly related to what Superfeedr is building but to what we’re seeing as a trend in the social applications field. About 10 years ago, a small company called Google wiped out any competing search engine with their PageRank algorithm. This one, without going into a lot of details was based on reputation of a web page : the more other pages have links to it, the more accurate it has to be.

If you’re a Facebook user (and haven’t quit yet :D), you’ve probably seen at the top of your News Feed, that you can choose between Top News and Most Recent. The latter is an exhaustive list of what all activities in your social graph, while the 1st one is only a subset of it. I believe the algorithm that is being used here is probably one of the few social filtering algorithm out there.

Why does it matter?

It’s no scoop to say that we’re slowly getting overwhelmed with data and activities in our networks. At the same time, this is data that we want to get, but data that we can’t deal with. This is what is extremely frustrating. I think it’s a tremendous value proposition for a service to provide me with the social data from my network in a way that I can consume in minutes, rather than hours.

Obviously this is very hard to build and even though I found Facebook algorithm ok, I keep clicking on Recent news to see the exhaustive news. I have also curated myself a lot of content (mainly by hiding apps, or fan pages that are too “verbose”). It’s probably harder to build because it’s a negative algorithm : it has to remove stuff, while the page rank and other algorithm are positive : they extract the relevant data.

If you’re doing any research on this topic, I’m sure you’ll find a job quite soon at any of the startups are trying to build out these social consuming applications.

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Previously, on the Superfeedr blog: Make your application real-time with PubSubHubbub.