
Directed Edge: a new take on social-filtering technologies [interview, video] - wheels
http://uk.intruders.tv/Directed-Edge-a-new-take-on-social-filtering-technologies_a479.html
======
dood
Looks like a promising beginning to a recommendation-engine startup.

It's good to see pure machine-learning based startups get going. I'm looking
forward to a lot of interesting stuff happening and massive growth in this
area over the next few years. Will be fun to be able to shove a load of data
through a variety of ML system's APIs at will.

------
Bjoern
The book he mentioned. Its worth a read if you are looking for a practical
introduction of Machine Learning/Classification etc. (currently on my desk)

<http://oreilly.com/catalog/9780596529321/>

~~~
wheels
I've got kind of a love-hate relationship with Programming Collective
Intelligence.

In many ways it's an awesome book. It covers a lot of territory with relative
grace and in clear language. On the other hand, it's often simplified to the
point that the versions of things covered in there aren't really suitable for
more than toy applications.

My fear is that it brings the low end of some of these fields in reach of
folks who aren't used to working with the sort of material that they'll need
to get up to the next notch -- which is really where the practical
applications begin. I suspect that could be frustrating for some.

------
llimllib
I'd love to see a bibliography of the papers he likes on the recommender
problem.

~~~
wheels
Unfortunately it'd be hard to make sense of a reading list that I'd put
together since unlike academic research where you tend to bore down deeper and
deeper along a certain path, I've been picking up ideas in more of a grab-bag
fashion. Some of the papers that I like aren't especially good papers, but
happened to be the connector between two ideas that I'd been kicking around.

One of the better general introductions, with a lot of good references at the
end is:

Google News Personalization: Scalable Online Collaborative Filtering
(<http://www2007.org/papers/paper570.pdf>)

That does take a significantly more traditional approach to recommendations
algorithms than we do and also the algorithms there tend to do best in cases
where (not surprising given the context) the number of users is much larger
than the number of items being rated.

------
auston
You guys should give the Netflix Prize a go!

