DISQUS

Sam Sethi · 2 years ago

Google meets Yahoo Answers or as I mentioned today LinkedIn answers with a micro[format]search engine. Alogrithmic search alone is not enough. Riya realised this with their face matching software not being quicker than a human recognising faces.

Paul · 2 years ago

I think you're on to something, and I think we (my company) may be on to something too. Good to hear we could be fundable one day ;) Cheers from across the pond,

Paul

leafar · 2 years ago

Nic,
What you talked about is Collaborative Filtering vs Content Based.
But mainly it's Human Behaviour (Lastfm) vs Fixed Information (Pandora's Genome).

The fight is being alive from a very long time among academics. They both have pros & cons. Wikipedia as a good explaination of it.

And from my point of view pandora & lastfm are a perfect couple !

That's why we've been mixing the two technologies together ... when we have data we use Collaborative when not we use Content Base.

But many challenges are facing both solutions. What if pandora was wikipedianlike, what if Lastfm known that two songs with different title ar in fact the same one ....

You need Human computation to face the Riya problem (cf. Sam's comment), you need Silicium Computation to do what can be automatic.

I agree a good answer will be to embrace both of them so you can use the fastest.

Think about illustration picture on lastfm. And try changing one on U.[lik]. Who is making the category on leiki ? Will it be right for me, or do I use a different tagging method ?

It's definitely not a black & white problem. Finding the perfect grey it's what really matter. And I haven't seen one yet (even @ home) but I'm looking for a Rothko's painting. Black on one side white on the other ... with a blur transition that creates fascination.
http://tn1-1.deviantart.com/fs4/100/i/2004/238/...

Andy Weissman · 2 years ago

Also see Carmun (www.carmun.com) for another approach -- using generally available taxonomies (the US library of congress, for example) then adding a layer of user generated data or knowledge to make it even more useful, and of course with that user, or meta, data comes the ability to add community aspects to what is traditionally an individual task -- studying

John Wilson · 2 years ago

Nic

I think there is an important sub-division in your "wisdom of crowds" category, between trust networks like LinkedIn and crowd sites which give you data on which you may or may not rely like Toptable reviews. Clearly the former is much stronger since you can rely on the information based on your assessment of the source. In the latter, popularity is your main guide, which doesn't necessarily equate to wisdom. Sam Sethi and I recently chatted about this and its impact on the long-term success of "social networks".

As regards machine recommendations, the ability of computers to spot correlations in behaviour to help make "insightful" suggestions I believe is a very powerful force but only to the extent that the dataset and population on which it draws is sufficiently sizeable to allow meaningful conclusions to be drawn. Too often social networks have/use insufficient data points in making comparisons to allow them to identify genuine correlations e.g. male & works in capital markets as our shared points isn't sufficient to guess our interest/tastes will overlap. Add on music, hobbies, education..... At which point we start to use something akin to "dating service" algorythms to identify people we "correspond" with and hence have increased likelihood that the machine recommendations will reasonate.

Philip Wilkinson · 2 years ago

You can always do both right - you take some basic explicit data if provided by a user, combine it with some clever algorithms for measuring implicit behaviour, draw some conclusions, enhance it again and again as the user does more explicit things.