

"We think you'd also like..." and the Math of Suggestion – Part 1 (from Directed Edge) - wheels
http://www.gruenderszene.de/it/we-think-youd-also-like-and-the-math-of-suggestion-–-teil-1/

======
chrislo
Ilya Grigorik wrote a great article on recommendation systems (with code in
Ruby):

[http://www.igvita.com/2007/01/15/svd-recommendation-
system-i...](http://www.igvita.com/2007/01/15/svd-recommendation-system-in-
ruby/)

~~~
davi
much better than the submitted article, thanks

~~~
wheels
For our own blog, which tends to be more tech-heavy, I'm open to writing on
some topics that people have specific interest in in the recommendations
world, so if there's stuff you're curious about I'm all ears. This article was
naturally geared more towards the audience of the place publishing it.

------
wheels
It's unfortunately split up into two parts due to requirements on the blog
that published it (literally "Founder Scene"), but this hits on some of the
basics of collaborative filtering. The next part gets into item-based
collaborative filtering and a little on graph-based stuff. I'll post the
second part here when it comes around.

The article is definitely targeted at being a quick introduction to how
recommendations work rather than being a how-to. Most Gründerszene's
subscribers aren't developers so it's an attempt to present the stuff in a
digestible format.

~~~
thomasfl
Is there a part two out there yet?

~~~
wheels
No, it's written, but not published yet. It was written as one article, but
since it was over their word limit, they split it into two parts. I think they
usually space them out by about a week.

------
ivyirwin
It will be nice to see part two, but there is enough fodder in part one to
talk about.

There is no doubt recommendation engines are a great sales generator. But are
they any good at what they do? Plotting points on a graph based on user
ratings has always seemed artificial to me. I may like a book because of its
rhythm and someone else may like the subject matter; will we like the same
books? This is why I think netflix created their competition. They're not
looking for better math, but for a better approach all together.

At the end of the day, it probably has to be a combination of factors. For
example, think of pandora's engine. It is based on analysis of the music, not
just user preferences. And yes, math will be involved, which is why part two
of this article will be worth reading.

~~~
wheels
The funny thing is that we're not using the standard approaches to
recommendations, though in an introduction to how the systems work in general,
it seemed best to take the simplest explanation. Part II has something on
graph-based approaches, but in general, the class of algorithms we're using
tends to, at least in my opinion, produce better results in a couple orders of
magnitude less time. I loved Greg's post here:

[http://cacm.acm.org/blogs/blog-cacm/22925-what-is-a-good-
rec...](http://cacm.acm.org/blogs/blog-cacm/22925-what-is-a-good-
recommendation-algorithm/fulltext)

It's a valid question to ask, "Will these algorithms really suss out my
personal taste?" And I think the answer is, a lot of the time, they'll get
close enough to be interesting.

When we started Directed Edge, one of the tipping point moments was seeing how
bad some of the related films shown in IMDB for my favorite film were. It was
a real watershed moment this week when I was running our algorithm against the
Netflix dataset after adding 15 of my own ratings to the system and my two
favorite films (which I had not rated) came out at #1 and #5, respectively,
and at that, with 300x less computation time than a standard item-to-item
recommender requires. (We've got implementations of a lot of standard
algorithms that we use for benchmarking our own.)

