

“We think you’d also like…” and the Math of Suggestion - Part 2 - wheels
http://www.gruenderszene.de/it/we-think-youd-also-like-and-the-math-of-suggestion-%E2%80%93-teil-2/

======
wheels
This is the continuation from here:

<http://news.ycombinator.com/item?id=548584>

[http://www.gruenderszene.de/it/we-think-youd-also-like-
and-t...](http://www.gruenderszene.de/it/we-think-youd-also-like-and-the-math-
of-suggestion-–-teil-1/)

As noted in the last round, this is a pretty light introduction to
recommendations targeted mostly at only moderately technical folks (the
audience of the blog that published it).

------
sgrove
It's a well written, if short, piece. I've never thought of recommendation
systems as _fun_ , but I feel like trying out some basic stuff with it now.

Anyone know a good way to get started - especially simple test data?

~~~
wheels
If you just want data sets drop me a line, though most of the ones we test
with are probably prohibitively large for just playing around with (millions
of links / ratings). O'Reilly's _Programming Collective Intelligence_ gives
some decent background information, but in my opinion doesn't really cover
enough to build a real system. A couple papers I often suggest with a lot of
practical content are:

 _Toward the Next Generation of Recommender Systems: A Survey of the State-of-
the-Art and Possible Extensions_

<http://portal.acm.org/citation.cfm?id=1070751>

That one's fairly readable for people outside of the field. The Google News
paper, which has some insights on doing large scale recommendations on a
fairly dense user to item matrix, is a little more jumping into the deep end,
but is worth glancing at even just to follow the references it sites:

 _Google News Personalization: Scalable Online Collaborative Filtering_

<http://www2007.org/papers/paper570.pdf>

(The paper itself doesn't mention being problematic on sparse rating sets, but
I've implemented something very similar and found that to be the case.)

