def calculate_users_similarity(self, user_dictionary, user1, user2):
common_items = extractor.get_common_items(user_dictionary, user1,
user2)
if not common_items:
return None
if self._min_common_items is not None and len(
common_items) < self._min_common_items:
return None
user1_overall_ratings = user_dictionary[user1].item_ratings
user1_multi_ratings = user_dictionary[user1].item_multi_ratings
user2_overall_ratings = user_dictionary[user1].item_ratings
user2_multi_ratings = user_dictionary[user2].item_multi_ratings
similarity_sum = 0.
for item in common_items:
user1_item_ratings = list(user1_multi_ratings[item])
user1_item_ratings.insert(0, user1_overall_ratings[item])
user2_item_ratings = list(user2_multi_ratings[item])
user2_item_ratings.insert(0, user2_overall_ratings[item])
similarity_sum += similarity_calculator.calculate_similarity(
user1_item_ratings, user2_item_ratings,
self._similarity_metric)
similarity = similarity_sum / len(common_items)
return similarity
def calculate_users_similarity(self, user_dictionary, user1, user2):
common_items = extractor.get_common_items(user_dictionary, user1, user2)
if not common_items:
return None
if self._min_common_items is not None and len(
common_items) < self._min_common_items:
return None
user1_overall_ratings = user_dictionary[user1].item_ratings
user1_multi_ratings = user_dictionary[user1].item_multi_ratings
user2_overall_ratings = user_dictionary[user1].item_ratings
user2_multi_ratings = user_dictionary[user2].item_multi_ratings
similarity_sum = 0.
for item in common_items:
user1_item_ratings = list(user1_multi_ratings[item])
user1_item_ratings.insert(0, user1_overall_ratings[item])
user2_item_ratings = list(user2_multi_ratings[item])
user2_item_ratings.insert(0, user2_overall_ratings[item])
similarity_sum += similarity_calculator.calculate_similarity(
user1_item_ratings, user2_item_ratings, self._similarity_metric)
similarity = similarity_sum / len(common_items)
return similarity
def calculate_users_similarity(self, user_dictionary, user1, user2):
common_items = extractor.get_common_items(user_dictionary, user1,
user2)
if not common_items:
return None
if self._min_common_items is not None and len(
common_items) < self._min_common_items:
return None
user1_overall_ratings =\
extractor.get_user_ratings(user_dictionary, user1, common_items)
user1_multi_ratings =\
extractor.get_user_multi_ratings(user_dictionary, user1, common_items)
user2_overall_ratings =\
extractor.get_user_ratings(user_dictionary, user2, common_items)
user2_multi_ratings =\
extractor.get_user_multi_ratings(user_dictionary, user2, common_items)
num_criteria = len(user1_multi_ratings[0])
total_similarity = 0.
for i in xrange(0, num_criteria):
user1_criterion_item_ratings =\
extractor.get_matrix_column(user1_multi_ratings, i)
user2_criterion_item_ratings =\
extractor.get_matrix_column(user2_multi_ratings, i)
total_similarity += similarity_calculator.calculate_similarity(
user1_criterion_item_ratings, user2_criterion_item_ratings,
self._similarity_metric)
# We also add the overall similarity
total_similarity += similarity_calculator.calculate_similarity(
user1_overall_ratings, user2_overall_ratings,
self._similarity_metric)
average_similarity = total_similarity / (num_criteria + 1)
return average_similarity
def calculate_users_similarity(self, user_dictionary, user1, user2):
common_items = extractor.get_common_items(user_dictionary, user1, user2)
if not common_items:
return None
if self._min_common_items is not None and len(
common_items) < self._min_common_items:
return None
user1_overall_ratings =\
extractor.get_user_ratings(user_dictionary, user1, common_items)
user1_multi_ratings =\
extractor.get_user_multi_ratings(user_dictionary, user1, common_items)
user2_overall_ratings =\
extractor.get_user_ratings(user_dictionary, user2, common_items)
user2_multi_ratings =\
extractor.get_user_multi_ratings(user_dictionary, user2, common_items)
num_criteria = len(user1_multi_ratings[0])
total_similarity = 0.
for i in xrange(0, num_criteria):
user1_criterion_item_ratings =\
extractor.get_matrix_column(user1_multi_ratings, i)
user2_criterion_item_ratings =\
extractor.get_matrix_column(user2_multi_ratings, i)
total_similarity += similarity_calculator.calculate_similarity(
user1_criterion_item_ratings, user2_criterion_item_ratings,
self._similarity_metric)
# We also add the overall similarity
total_similarity += similarity_calculator.calculate_similarity(
user1_overall_ratings, user2_overall_ratings, self._similarity_metric)
average_similarity = total_similarity / (num_criteria + 1)
return average_similarity
def calculate_users_similarity(self, user_dictionary, user_id1, user_id2):
"""
Calculates the similarity between two users based on how similar are
their ratings in the reviews
:param user_id1: the ID of user 1
:param user_id2: the ID of user 2
:return: a float with the similarity between the two users. Since this
function is based on euclidean distance to calculate the similarity, a
similarity of 0 indicates that the users share exactly the same tastes
"""
user_weights1 = user_dictionary[user_id1].criteria_weights
user_weights2 = user_dictionary[user_id2].criteria_weights
return similarity_calculator.calculate_similarity(
user_weights1, user_weights2, self._similarity_metric)
def calculate_users_similarity(self, user_dictionary, user1, user2):
common_items = extractor.get_common_items(user_dictionary, user1, user2)
if not common_items:
return None
if self._min_common_items is not None and len(
common_items) < self._min_common_items:
return None
user1_ratings =\
extractor.get_user_ratings(user_dictionary, user1, common_items)
user2_ratings =\
extractor.get_user_ratings(user_dictionary, user2, common_items)
similarity_value = similarity_calculator.calculate_similarity(
user1_ratings, user2_ratings, self._similarity_metric)
return similarity_value