The State of the Running for the Netflix Prize

Over at Statistical Modeling, Causal Inference, and Social Science, Aleks Jakulin covers the race for the Netflix prize:


Many of you buy and rank books, movies on the web, you click on links, bookmark them, blog about them. By doing this, you are leaving traces behind. The traces are of great help to those who will find themselves in the same situation as you. Personalization technology tries to help you navigate the choices using the actions of people who were there before you, and with the the implicit (clicks or purchases you’ve made) or explicit (preferences you’ve expressed) knowledge about yourself.

Greg Linden’s blog is an excellent source of insightful posts on personalization technology. A while ago he posted a link to a collection of material from KDD about the Netflix Prize: a challenge where one has to predict how much you will like a particular movie based on your history of movies you’ve seen and based on others’ ratings of movies they’ve seen.

What’s notable is that some of the current competition leaders have written extensive papers about their approach. BellKor’s approach is quite simple and combines nearest-neighbor ideas with a more global factor model. On the other hand, Gravity employs a diverse collection of tools, including matrix factorization, neural networks, nearest neighbor models and clustering. The Gravity team provides an interesting picture of their factor model for movie Constantine.