There is no longer any reason to bother researching new ways of predicting the ratings users will give to movies. It’s time to move on to more interesting things. But seriously, given the fact that the last few miles of the Netflix competition were hard-fought by combining hundreds of different algorithms, is there much value in trying to improve recommender systems in this way, anymore?
I expect that the Netflix Prize data set, if left open to the public, will still be useful for a number of machine learning tasks where the goal is not necessarily improving recommender systems. So predicting movie ratings may never be really dead. But it is my hope that that as a goal for research will diminish and the focus will start moving towards other aspects of recommender systems still greatly lacking. Like building systems that facilitate discovery of new items.
Factoring in the temporal dimension was a big deal in the latter part of the competition. Sometimes you’re just in the mood for something gloomy. Or something funny. Or something ridiculous. The same movie may totally turn you off a week later. No machine (biological or mechanical) can predict these swings of emotions in the near future, so why bother? Flip that around and let’s find ways of improving the search for items matching our mood at the time.
A system that interactively elicits your mood and guides you to matching items would be incredibly useful, don’t you think?