MLlib collaborative filtering to generate Top N recommendations

Question

I was looking to find out a way to generate top n recommendations for all users using MLlib's ALS matrix factorization, but remained unsuccessful. Can anybody tell me does any such method exist?

Answer 1

here is my current approach which seems to be extremely slow:

Iterator<Rating> it = ratings.toLocalIterator();
        while (it.hasNext()) {
            int user = it.next().user();
            if (!userList.contains(user)) {
                Rating[] rat = model.recommendProducts(user, 10);
                for (Rating r : rat) {
                    list.add(user + "," + r.product() + "," + r.rating());
                }
                userList.add(user);
            }
        }

any efficient approach would be greatly appreciated.