def get_neighbourhood(self, user, item, context, threshold):
all_users = self.user_ids[:]
all_users.remove(user)
neighbours = []
# We remove the users who have not rated the given item
for neighbour in all_users:
if item in self.user_dictionary[neighbour].item_ratings:
neighbours.append(neighbour)
neighbour_similarity_map = {}
for neighbour in neighbours:
neighbour_context =\
self.user_dictionary[neighbour].item_contexts[item]
context_similarity = context_utils.get_context_similarity(
context, neighbour_context, self.topic_indices)
if context_similarity > threshold:
neighbour_similarity_map[neighbour] = context_similarity
# Sort the users by similarity
neighbourhood = dictionary_utils.sort_dictionary_keys(
neighbour_similarity_map) # [:self.num_neighbors]
if self.num_neighbours is None:
return neighbourhood
return neighbourhood
def get_neighbourhood(self, user, item, context, threshold):
all_users = self.user_ids[:]
all_users.remove(user)
neighbours = []
# We remove the users who have not rated the given item
for neighbour in all_users:
if item in self.user_dictionary[neighbour].item_ratings:
neighbours.append(neighbour)
neighbour_similarity_map = {}
for neighbour in neighbours:
neighbour_context =\
self.user_dictionary[neighbour].item_contexts[item]
context_similarity = context_utils.get_context_similarity(
context, neighbour_context, self.topic_indices)
if context_similarity > threshold:
neighbour_similarity_map[neighbour] = context_similarity
# Sort the users by similarity
neighbourhood = dictionary_utils.sort_dictionary_keys(
neighbour_similarity_map) # [:self.num_neighbors]
if self.num_neighbours is None:
return neighbourhood
return neighbourhood
def get_rating_on_context(self, user, item, context, threshold):
neighbour_context = self.user_dictionary[user].item_contexts[item]
context_similarity = context_utils.get_context_similarity(
context, neighbour_context, self.topic_indices)
if context_similarity < threshold:
return None
return self.user_dictionary[user].item_ratings[item]
def get_rating_on_context(self, user, item, context, threshold):
neighbour_context = self.user_dictionary[user].item_contexts[item]
context_similarity = context_utils.get_context_similarity(
context, neighbour_context, self.topic_indices)
if context_similarity < threshold:
return None
return self.user_dictionary[user].item_ratings[item]
def calculate_user_similarity(self, user1, user2, threshold):
common_items = extractor.get_common_items(
self.user_dictionary, user1, user2)
if not common_items:
return None
filtered_items = {}
for item in common_items:
context1 = self.user_dictionary[user1].item_contexts[item]
context2 = self.user_dictionary[user2].item_contexts[item]
context_similarity = context_utils.get_context_similarity(
context1, context2, self.context_rich_topics)
if context_similarity > threshold:
filtered_items[item] = context_similarity
numerator = 0
denominator1 = 0
denominator2 = 0
denominator3 = 0
user1_average = self.user_dictionary[user1].average_overall_rating
user2_average = self.user_dictionary[user2].average_overall_rating
for item in filtered_items.keys():
context_similarity = filtered_items[item]
user1_rating = self.user_dictionary[user1].item_ratings[item]
user2_rating = self.user_dictionary[user2].item_ratings[item]
numerator +=\
(user1_rating - user1_average) *\
(user2_rating - user2_average) *\
context_similarity
denominator1 += (user1_rating - user1_average) ** 2
denominator2 += (user2_rating - user2_average) ** 2
denominator3 += context_similarity ** 2
denominator = math.sqrt(denominator1 * denominator2 * denominator3)
if denominator == 0:
return None
return numerator / denominator
def calculate_user_similarity(self, user1, user2, threshold):
common_items = extractor.get_common_items(self.user_dictionary, user1,
user2)
if not common_items:
return None
filtered_items = {}
for item in common_items:
context1 = self.user_dictionary[user1].item_contexts[item]
context2 = self.user_dictionary[user2].item_contexts[item]
context_similarity = context_utils.get_context_similarity(
context1, context2, self.context_rich_topics)
if context_similarity > threshold:
filtered_items[item] = context_similarity
numerator = 0
denominator1 = 0
denominator2 = 0
denominator3 = 0
user1_average = self.user_dictionary[user1].average_overall_rating
user2_average = self.user_dictionary[user2].average_overall_rating
for item in filtered_items.keys():
context_similarity = filtered_items[item]
user1_rating = self.user_dictionary[user1].item_ratings[item]
user2_rating = self.user_dictionary[user2].item_ratings[item]
numerator +=\
(user1_rating - user1_average) *\
(user2_rating - user2_average) *\
context_similarity
denominator1 += (user1_rating - user1_average)**2
denominator2 += (user2_rating - user2_average)**2
denominator3 += context_similarity**2
denominator = math.sqrt(denominator1 * denominator2 * denominator3)
if denominator == 0:
return None
return numerator / denominator
def calculate_neighbour_contribution(
self, neighbour_id, item_id, context, threshold):
neighbour_rating = self.user_baseline_calculator.get_rating_on_context(
neighbour_id, item_id, context, threshold)
neighbor_average =\
self.user_baseline_calculator.calculate_user_baseline(
neighbour_id, context, threshold)
neighbour_context =\
self.user_baseline_calculator.user_dictionary[neighbour_id].item_contexts[item_id]
context_similarity = context_utils.get_context_similarity(
context, neighbour_context, self.user_baseline_calculator.topic_indices)
if not neighbour_rating:
return None
if not neighbor_average:
return None
return (neighbour_rating - neighbor_average) * context_similarity
def calculate_neighbour_contribution(self, neighbour_id, item_id, context,
threshold):
neighbour_rating = self.user_baseline_calculator.get_rating_on_context(
neighbour_id, item_id, context, threshold)
neighbor_average =\
self.user_baseline_calculator.calculate_user_baseline(
neighbour_id, context, threshold)
neighbour_context =\
self.user_baseline_calculator.user_dictionary[neighbour_id].item_contexts[item_id]
context_similarity = context_utils.get_context_similarity(
context, neighbour_context,
self.user_baseline_calculator.topic_indices)
if not neighbour_rating:
return None
if not neighbor_average:
return None
return (neighbour_rating - neighbor_average) * context_similarity
