Personal Relevancy

Fred Wilson from AVC has recently posted an article about 'Is Meta Better'. The question is do services that aggregate group interest to calculate 'the front page' of content (e.g. TechMeme, Digg etc) constitute a better way of consuming news and managing attention.

The answer for me is a resounding ‘not by itself’. While TechMeme, Digg and others have their place in the eco-system (to discover things you might not have known before or to measure where the buzz is) the real attention killer app is something I am starting to call 'Personal Relevancy'.

Personal Relevancy is about letting you choose the sources you subscribe to and then applying a filter to the incoming items so that the most important items rise to the top of the pack.

This can be done by analysing and modelling a single user's behaviours and interests as apposed to aggregating a broad community’s behaviours and interests.

This is the long tail of relevancy and attention - it is not about the top 10, its about the next 100,000. It is not what 100,000 people find interesting, it's about what YOU find interesting.

Via Attention Trust

Update:

Ed Batista has posted about my post about his post (thanks Ed!) and highlighted a lack of thoroughness above (thanks Ed hah!).

The wrinkle I'd add is that it's all contextual: Sometimes we're going to be interested in items from our personal Long Tail, sometimes we're going to be interested in items on the global Top 10 list, and sometimes we're going to interested in items derived from some sub-community in between--friends and family, co-workers, teammates, etc. We should ultimately be able to mix and match the sources powering our discovery and recommendation systems to suit our needs at the moment. But Chris is right to note that it all starts with what's personally relevant and builds from there.

He is 100% correct. Both that I had missed some key aspects of personal relevancy and the specific aspects he listed.

At Touchstone we are actually planning to include all these factors in the calculation of an item's personal relevancy. We call this algorithm I-AM (Intuitive Attention Management). I-AM has evolved a lot since we originally announced it to truly embody it's descriptive paragraph:

"I-AM" who I know.
"I-AM" what I read.
"I-AM" where I am.
"I-AM" how I work.
"I-AM" me.

I-AM is all about measuring your personal behaviour, the behaviour of who you know, the collective interests of the broader community, the situation you are actually in, the device you're on and how busy you are to determine personal relevancy at any given time.