def user_neighborhood(self, user_id, data_model, n_similarity='user_similarity',
             distance=None, nhood_size=None, **params):
        minimal_similarity = params.get('minimal_similarity', 0.0)
        sampling_rate = params.get('sampling_rate', 1.0)

        data_model = self._sampling(data_model, sampling_rate)
        #set the nhood_size at Similarity , and use Similarity to get the top_users
        if distance is None:
            distance = euclidean_distances
        if n_similarity == 'user_similarity':
            self._set_similarity(data_model, n_similarity, distance, nhood_size)
        else:
            raise ValueError('similarity argument must be user_similarity')

        neighborhood = [to_user_id for to_user_id, score in self.similarity[user_id] \
                           if not np.isnan(score) and score >= minimal_similarity and user_id != to_user_id]

        return neighborhood
Sets the time this vulnerability was published, represented as
dataset/ETHPy150Open muricoca/crab/scikits/crab/recommenders/knn/neighborhood_strategies.py/NearestNeighborsStrategy.user_neighborhood--crossover with--dataset/ETHPy150Open STIXProject/python-stix/stix/exploit_target/vulnerability.py/Vulnerability.published_datetime