def __init__(self,
name,
similarity_metric=None,
significant_criteria_ranges=None):
super(MultiCriteriaBaseRecommender, self).__init__(name, None)
self._significant_criteria_ranges = significant_criteria_ranges
self._similarity_matrix_builder = WeightsSimilarityMatrixBuilder(
similarity_metric)
self.user_cluster_dictionary = None
def __init__(self, name, similarity_metric=None, significant_criteria_ranges=None):
super(MultiCriteriaBaseRecommender, self).__init__(name, None)
self._significant_criteria_ranges = significant_criteria_ranges
self._similarity_matrix_builder = WeightsSimilarityMatrixBuilder(similarity_metric)
self.user_cluster_dictionary = None
class MultiCriteriaBaseRecommender(BaseRecommender):
__metaclass__ = ABCMeta
def __init__(self, name, similarity_metric=None, significant_criteria_ranges=None):
super(MultiCriteriaBaseRecommender, self).__init__(name, None)
self._significant_criteria_ranges = significant_criteria_ranges
self._similarity_matrix_builder = WeightsSimilarityMatrixBuilder(similarity_metric)
self.user_cluster_dictionary = None
def load(self, reviews):
self.reviews = reviews
self.user_ids = extractor.get_groupby_list(self.reviews, "user_id")
self.user_dictionary = extractor.initialize_cluster_users(self.reviews, self._significant_criteria_ranges)
self.user_cluster_dictionary = self.build_user_clusters(self.reviews, self._significant_criteria_ranges)
if self._similarity_matrix_builder._similarity_metric is not None:
self.user_similarity_matrix = self._similarity_matrix_builder.build_similarity_matrix(
self.user_dictionary, self.user_ids
)
def clear(self):
super(MultiCriteriaBaseRecommender, self).clear()
self.user_cluster_dictionary = None
# TODO: Add the item_id as a parameter in order to optimize the method
def get_neighbourhood(self, user_id):
cluster_name = self.user_dictionary[user_id].cluster
cluster_users = list(self.user_cluster_dictionary[cluster_name])
cluster_users.remove(user_id)
# We remove the given user from the cluster in order to avoid bias
if self._num_neighbors is None:
return cluster_users
similarity_matrix = self.user_similarity_matrix[user_id].copy()
similarity_matrix.pop(user_id, None)
ordered_similar_users = dictionary_utils.sort_dictionary_keys(similarity_matrix)
intersection_set = set.intersection(set(ordered_similar_users), set(cluster_users))
intersection_lst = [t for t in ordered_similar_users if t in intersection_set]
return intersection_lst # [:self._num_neighbors]
@staticmethod
def build_user_clusters(reviews, significant_criteria_ranges=None):
"""
Builds a series of clusters for users according to their significant
criteria. Users that have exactly the same significant criteria will belong
to the same cluster.
:param reviews: the list of reviews
:return: a dictionary where all the keys are the cluster names and the
values for those keys are list of users that belong to that cluster
"""
user_list = extractor.get_groupby_list(reviews, "user_id")
user_cluster_dictionary = {}
for user in user_list:
weights = extractor.get_criteria_weights(reviews, user)
significant_criteria, cluster_name = extractor.get_significant_criteria(
weights, significant_criteria_ranges
)
if cluster_name in user_cluster_dictionary:
user_cluster_dictionary[cluster_name].append(user)
else:
user_cluster_dictionary[cluster_name] = [user]
return user_cluster_dictionary
class MultiCriteriaBaseRecommender(BaseRecommender):
__metaclass__ = ABCMeta
def __init__(self,
name,
similarity_metric=None,
significant_criteria_ranges=None):
super(MultiCriteriaBaseRecommender, self).__init__(name, None)
self._significant_criteria_ranges = significant_criteria_ranges
self._similarity_matrix_builder = WeightsSimilarityMatrixBuilder(
similarity_metric)
self.user_cluster_dictionary = None
def load(self, reviews):
self.reviews = reviews
self.user_ids = extractor.get_groupby_list(self.reviews, 'user_id')
self.user_dictionary =\
extractor.initialize_cluster_users(self.reviews, self._significant_criteria_ranges)
self.user_cluster_dictionary = self.build_user_clusters(
self.reviews, self._significant_criteria_ranges)
if self._similarity_matrix_builder._similarity_metric is not None:
self.user_similarity_matrix =\
self._similarity_matrix_builder.build_similarity_matrix(
self.user_dictionary, self.user_ids)
def clear(self):
super(MultiCriteriaBaseRecommender, self).clear()
self.user_cluster_dictionary = None
# TODO: Add the item_id as a parameter in order to optimize the method
def get_neighbourhood(self, user_id):
cluster_name = self.user_dictionary[user_id].cluster
cluster_users = list(self.user_cluster_dictionary[cluster_name])
cluster_users.remove(user_id)
# We remove the given user from the cluster in order to avoid bias
if self._num_neighbors is None:
return cluster_users
similarity_matrix = self.user_similarity_matrix[user_id].copy()
similarity_matrix.pop(user_id, None)
ordered_similar_users = dictionary_utils.sort_dictionary_keys(
similarity_matrix)
intersection_set = set.intersection(set(ordered_similar_users),
set(cluster_users))
intersection_lst = [
t for t in ordered_similar_users if t in intersection_set
]
return intersection_lst # [:self._num_neighbors]
@staticmethod
def build_user_clusters(reviews, significant_criteria_ranges=None):
"""
Builds a series of clusters for users according to their significant
criteria. Users that have exactly the same significant criteria will belong
to the same cluster.
:param reviews: the list of reviews
:return: a dictionary where all the keys are the cluster names and the
values for those keys are list of users that belong to that cluster
"""
user_list = extractor.get_groupby_list(reviews, 'user_id')
user_cluster_dictionary = {}
for user in user_list:
weights = extractor.get_criteria_weights(reviews, user)
significant_criteria, cluster_name =\
extractor.get_significant_criteria(weights, significant_criteria_ranges)
if cluster_name in user_cluster_dictionary:
user_cluster_dictionary[cluster_name].append(user)
else:
user_cluster_dictionary[cluster_name] = [user]
return user_cluster_dictionary
