The "make_recommendations" method from my ALS recommender

make_recommendations_als.py

def make_recommendations(self, fav_movie, n_recommendations):
    """
    make top n movie recommendations
    Parameters
    ----------
    fav_movie: str, name of user input movie
    n_recommendations: int, top n recommendations
    """
    # get data
    movie_user_mat_sparse, hashmap = self._prep_data()
    # get recommendations
    raw_recommends = self._inference(
        self.model, movie_user_mat_sparse, hashmap,
        fav_movie, n_recommendations)
    # print results
    reverse_hashmap = {v: k for k, v in hashmap.items()}
    print('Recommendations for {}:'.format(fav_movie))
    for i, (idx, dist) in enumerate(raw_recommends):
        print('{0}: {1}, with distance '
              'of {2}'.format(i+1, reverse_hashmap[idx], dist))

For entire source code of ALS recommender, please visit this page