def _get_pairwise_all_likefism_data(dataset):
user_input_pos, user_input_neg, num_idx_pos, num_idx_neg, item_input_pos, item_input_neg = [], [], [], [], [], []
num_items = dataset.num_items
num_users = dataset.num_users
train_matrix = dataset.train_matrix
for u in range(num_users):
items_by_u = train_matrix[u].indices.copy().tolist()
num_items_by_u = len(items_by_u)
if num_items_by_u > 1:
negative_items = randint_choice(num_items,
num_items_by_u,
replace=True,
exclusion=items_by_u)
for index, i in enumerate(items_by_u):
j = negative_items[index]
user_input_neg.append(items_by_u)
num_idx_neg.append(num_items_by_u + 1)
item_input_neg.append(j)
items_by_u.remove(i)
user_input_pos.append(items_by_u)
num_idx_pos.append(num_items_by_u)
item_input_pos.append(i)
return user_input_pos, user_input_neg, num_idx_pos, num_idx_neg, item_input_pos, item_input_neg
def _get_pairwise_all_likefossil_data(dataset, high_order, train_dict):
user_input_id,user_input_pos,user_input_neg, num_idx_pos, num_idx_neg, item_input_pos,item_input_neg,item_input_recents = [],[], [], [],[],[],[],[]
for u in range(dataset.num_users):
items_by_user = train_dict[u].copy()
num_items_by_u = len(items_by_user)
if num_items_by_u > high_order:
negative_items = randint_choice(dataset.num_items,
num_items_by_u,
replace=True,
exclusion=items_by_user)
for idx in range(high_order, len(train_dict[u])):
i = train_dict[u][idx] # item id
item_input_recent = []
for t in range(1, high_order + 1):
item_input_recent.append(train_dict[u][idx - t])
item_input_recents.append(item_input_recent)
j = negative_items[idx]
user_input_neg.append(items_by_user)
num_idx_neg.append(num_items_by_u)
item_input_neg.append(j)
items_by_user.remove(i)
user_input_id.append(u)
user_input_pos.append(items_by_user)
num_idx_pos.append(num_items_by_u - 1)
item_input_pos.append(i)
return user_input_id, user_input_pos, user_input_neg, num_idx_pos, num_idx_neg, item_input_pos, item_input_neg, item_input_recents