I have a training data set with 10m user ratings of movies, (expanded MovieLens) and 25 features (movie information, release, genre etc).
I want to design and build a recommendation system that will predict user ratings on movies, and recommend their "top x unseen movies".
What algorithms shall I use to predict user ratings on items, they take a set scale from 0.5 to 5.
I've read a lot of literature amount techniques, SVD, Decision Trees, Collaborative Filtering etc, but I'm finding it hard to choose a specific model.