Anyone with a handle on Business Intelligence should be looking at the Netflix Prize. Netflix is offering $1 million to the first team that can make a 10% improvement in their Cinematch movie recommendation system. Moreover, just by entering, you get access to a large dataset of movies and specific customer recommendations, something that should get any numbers jockey salivating.
Having looked at the competition rules carefully, I see that as usual the competition is somewhat tangential to the goal. The competition is to predict how people will rate movies based on their existing movie ratings, while the goal is to improve the recommendation system.
I am a long time Netflix user and I know that their recommendation system has some problems. The first improvement that I would suggest is to not recommend a movie to someone who has already rented the movie from Netflix. I am sure that more than 10% of all the recommendations I see are movies that I have already rented.
I should say by way of disclosure that I never rate movies. My Netflix queue is more than 2 years long, I have plenty of other sources of recommendations and I do not feel the need to do anything more to make my queue grow longer. However I do glance at the recommendations made after adding a movie to the queue and sometimes add movies from them.
There is a bigger discussion here. The Cinematch system judges peoples interest in a movie by what they say they think of movies. It would be much more effective if it also judged people by what they have done, whether they have already seen the movie, what types of movie they add to their queue and what kinds of movies they promote to the top of their queue. We know that what people say and do are often not the same and that actions speak louder than words, so lets take more heed of what they do and less of what they say.