Clustering before regression - recommender system
Clustering users makes sense. But if your only feature is the rating, I don't think it could produce a useful model for prediction. Below are my assumptions to make this justification:

The quality of movie should be distributed with a gaussion distribution.
If we look at the rating distribution of a common user, it should be something like gaussian.
I don't exclude the possibility that a few users only give ratings when they see a bad movie (thus all low ratings); and vice versa. But on a large scale of users, this should be unusual behavior. 

Thus I can imagine that after clustering, you get small groups of users in the two extreme cases; and most users are in the middle (because they share the gaussian-like rating behavior). Using this model, you probably get good results for users in the two small (extreme) groups; however for the majority of users, you cannot expect good predictions.
I have a file called train.dat which has three fields - userID, movieID and rating. 
I need to predict the rating in the test.dat file based on this.
I want to know how I can use scikit-learn's KMeans to group similar users given that I have only feature - rating. 
Does this even make sense to do? After the clustering step, I could do a regression step to get the ratings for each user-movie pair in test.dat
Edit: I have some extra files which contain the actors in each movie, the directors and also the genres that the movie falls into. I'm unsure how to use these to start with and I'm asking this question because I was wondering whether it's possible to get a simple model working with just rating and then enhance it with the other data. I read that this is called content based recommendation. I'm sorry, I should've written about the other data files as well.