def get_user_neighbours(self, user, item):
sim_users_matrix = self.similarity_matrix[user].copy()
sim_users_matrix.pop(user, None)
# We remove the users who have not rated the given item
sim_users_matrix = {
k: v for k, v in sim_users_matrix.items()
if item in self.ratings_matrix[k]}
# We remove neighbours that don't have a similarity with user
sim_users_matrix = {
k: v for k, v in sim_users_matrix.items()
if v}
# print(sim_users_matrix)
# Sort the users by similarity
neighbourhood = dictionary_utils.sort_dictionary_keys(
sim_users_matrix) # [:self.num_neighbors]
if self.num_neighbors:
neighbourhood = neighbourhood[:self.num_neighbors]
# print('neighbourhood', neighbourhood)
# print('neighbourhood size:', len(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_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_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]
def get_neighbourhood(self, user_id, item_id):
# if self._num_neighbors is None:
# neighbourhood = list(self.user_ids)
# neighbourhood.remove(user_id)
# return neighbourhood
similarity_matrix = self.user_similarity_matrix[user_id].copy()
similarity_matrix.pop(user_id, None)
# We remove the users who have not rated the given item
similarity_matrix = {
k: v for k, v in similarity_matrix.items()
if item_id in self.user_dictionary[k].item_ratings}
# Sort the users by similarity
neighbourhood = dictionary_utils.sort_dictionary_keys(
similarity_matrix) # [:self._num_neighbors]
return neighbourhood
def get_neighbourhood2(self, user, item, context, threshold):
sim_users_matrix = self.similarity_matrix[user].copy()
sim_users_matrix.pop(user, None)
# We remove the users who have not rated the given item
sim_users_matrix = {
k: v
for k, v in sim_users_matrix.items()
if item in self.ratings_matrix[k]
}
# We remove neighbours that don't have a similarity with user
sim_users_matrix = {k: v for k, v in sim_users_matrix.items() if v}
# Sort the users by similarity
neighbourhood = dictionary_utils.sort_dictionary_keys(sim_users_matrix)
if self.num_neighbors:
return neighbourhood[:self.num_neighbors]
return neighbourhood
def get_neighbourhood(self, user_id, item_id):
# if self._num_neighbors is None:
# neighbourhood = list(self.user_ids)
# neighbourhood.remove(user_id)
# return neighbourhood
similarity_matrix = self.user_similarity_matrix[user_id].copy()
similarity_matrix.pop(user_id, None)
# We remove the users who have not rated the given item
similarity_matrix = {
k: v
for k, v in similarity_matrix.items()
if item_id in self.user_dictionary[k].item_ratings
}
# Sort the users by similarity
neighbourhood = dictionary_utils.sort_dictionary_keys(
similarity_matrix) # [:self._num_neighbors]
return neighbourhood
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]
def get_neighbourhood2(self, user, item, context, threshold):
sim_users_matrix = self.similarity_matrix[user].copy()
sim_users_matrix.pop(user, None)
# We remove the users who have not rated the given item
sim_users_matrix = {
k: v for k, v in sim_users_matrix.items()
if item in self.ratings_matrix[k]}
# We remove neighbours that don't have a similarity with user
sim_users_matrix = {
k: v for k, v in sim_users_matrix.items()
if v}
# Sort the users by similarity
neighbourhood = dictionary_utils.sort_dictionary_keys(
sim_users_matrix)
if self.num_neighbors:
return neighbourhood[:self.num_neighbors]
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.reviews_matrix[neighbour]:
neighbours.append(neighbour)
neighbour_similarity_map = {}
for neighbour in neighbours:
neighbour_context = self.context_matrix[neighbour][item]
context_similarity = get_context_similarity(
context, neighbour_context, self.context_rich_topics)
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]
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.reviews_matrix[neighbour]:
neighbours.append(neighbour)
neighbour_similarity_map = {}
for neighbour in neighbours:
neighbour_context = self.context_matrix[neighbour][item]
context_similarity = get_context_similarity(
context, neighbour_context, self.context_rich_topics)
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]
return neighbourhood
