Exemplar Programming

An interesting take on machine learning over at the machine learning theory blog:

Exemplar Programming

Here's a snip:

Machine learning can be thought of as exemplar programming. Exemplar programming is creating examples of a (input,output) pairs which are used by an algorithm to predict a function from input to output.

[...]

Exemplar programming is a viewpoint of machine learning which (mostly) ignores statistics, prior information, and the possibility of overfitting. That’s a great deal to ignore, but there are gains as well.

jase.

NetflixPrize

Netflix the DVD rental company recently announced their NetflixPrize in which they will give away $1 million dollars for an algorithm that can out-perform their recommendation approach Cinematch by 10%. Given that this competition has caused a little bit of a stir in our research group I thought I would put this post together to capture a number of useful resources.

• NetflixPrize forum, FAQ, Rules, Leaderboard
  
• Wikipedia references on Collaborative Filtering (CF) and Recommendation Systems
• A video talk by a Netflix engineer on their Cinematch algorithm
• A collection of links about Cinematch
• Collections of papers on CF here, here, and here
• Research groups working on CF here
  

There are many many papers out there on collaborative filtering and recommendation systems. Here are a few good review and seminal CF works:

• Evaluating Collaborative Filtering Recommender Systems (2004)
• Social Information Filtering - Algorithms for Automating Word of Mouth (1995)
• Toward the next generation of recommender systems - a survey of the state-of-the-art and possible extensions (2005)
• Eigentaste - A Constant Time Collaborative Filtering Algorithm (2001)
• Content-Boosted Collaborative Filtering for Improved Recommendations (2002)
• and of course a google scholar search on CF
  

If/when you find any more useful resources, add them to the comments of this post!

jase.