Making relevancy relevant

Clearly with the world awash in information, relevancy continues to grow in importance. Because otherwise we’re just drowning in one another’s crap 🙂

As I see it, Google were the masters of relevancy and approached the concept from an engineering mindset. They are all about the science of relevance. The reverse engineering of our behaviour, the interpretation of and cross-matching within massive datasets, other clever shit I don’t or won’t ever understand.

Then my co-owner Jenni did this wonderful talk for a while to our clients about The Shift from Relevance to Resonance which celebrated the possibility that Twitter, who made noises back then about putting Resonance at the heart of their advertising platform (and therefore, right in the core of their DNA).

For Jenni the rise of Resonance was the possibility of a re-emergence of storytelling and humanity and goodness and stuff.

Resonance as an idea is not so much predictive and based on computation, but dynamic and based on human behaviour right there and then – if lots of people were retweeting a tweet, for example, it was a sign of resonance (I simplify, probably, but hopefully you get the idea if you didn’t already!).

But for me the problem isn’t solved yet. At all.

In Facebook I get tired of only seeing the same old (lovely, friendly, trusted) faces – it concerns the greedy infovore in me that I might be missing looser ties and edgier less-obvious connections to interesting information or opportunities. I feel stuck in self-reinforcing feedback loop thing that sees me happily stuck in a tiny sub-set of a much bigger network.

In Twitter I get concerned that my attention gravitates towards those who happen to be around at the same time, or who tweet more frequently – something my trip to SF really showed up (Twitter was super-quiet out there – my network of people in non-GMT time zones is somewhat lacking!).

In my opinion there’s a filtering problem here that still remains a huge, untapped opportunity.

With that thought in mind I tweeted this morning:

How long before Twitter or a 3rd party create an Edgerank overlay for Twitter to filter in and out ‘relevant’ tweets?

(To understand what Edgerank is, read this).

I got two interesting replies.

From my colleague Caz Yetman:

isn’t relevancy like beauty? – In the ‘eye of the beholder’ (Fuck yes! And how on earth can that be algorithmized?!)

From Duncan Birch:

interesting point but how would the relevancy be measured? via RT’s , interactions, clicks? (Yes, that’s right – the same jaded old basket of characteristics from which ‘relevance’ seems so often to be currently derived.).

I’d also watched this great TED talk by Eli Pariser on ‘Beward online filter bubbles’ yesterday, and I totally agree that we’ve got to be careful what filtering is happening that we don’t know about and cannot influence.

Finally it’s worth mentioning Google’s Priority Inbox, which I know some people are really rating, where the filtering mixes both the engineered and the human ‘training’. But it didn’t work for me – maybe I didn’t trust enough, lean in enough – I just couldn’t trust it to capture the diversity and unpredictability of what is important for me.

So I’m interested to see what emerges in this space.

What filtering patterns or tools will emerge, and – to Pariser’s points – who will they really work for? How will algorithms mix today’s relevance with serendipity, wildcards, opportunities from the edges of our interests and networks?

The challenge is capturing that sense of the bold curator, sommelier, stylist or other handpicker and recommender – and one that doesn’t just follow the path but knows when to chuck in the random play, the radical alternative. Then I’ll rest easy.

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