import graphlab

data = graphlab.SFrame.read_csv("http://s3.amazonaws.com/dato-datasets/movie_ratings/training_data.csv", column_type_hints={"rating":int})
data.head()

# Build a default recommender (a Matrix Factorization model)
# The data needs to contain at least three columns: user, item, and rating.
# All other columns in the dataset are ignored by the default recommender.
model = graphlab.recommender.create(data, user_id="user", item_id="movie", target="rating")

# You can now make recommendations for all the users you've just trained on
results = model.recommend(users=None, k=5)
results.head()

# Save the model for later use
model.save("my_model")