np.mean(recall))
result_out.write('\t'.join([
str(cnt),
str(uid), ','.join([str(lid) for lid in predicted])
]) + '\n')
if __name__ == '__main__':
data_dir = "../data/"
size_file = data_dir + "Gowalla_data_size.txt"
check_in_file = data_dir + "Gowalla_checkins.txt"
train_file = data_dir + "Gowalla_train.txt"
tune_file = data_dir + "Gowalla_tune.txt"
test_file = data_dir + "Gowalla_test.txt"
social_file = data_dir + "Gowalla_social_relations.txt"
poi_file = data_dir + "Gowalla_poi_coos.txt"
user_num, poi_num = open(size_file, 'r').readlines()[0].strip('\n').split()
user_num, poi_num = int(user_num), int(poi_num)
top_k = 100
alpha = 0.1
beta = 0.1
U = UserBasedCF()
S = FriendBasedCF(eta=0.05)
G = PowerLaw()
main()
recall.append(recallk(actual, predicted[:10]))
print(cnt, uid, "pre@10:", np.mean(precision), "rec@10:",
np.mean(recall))
result_out.write('\t'.join([
str(cnt),
str(uid), ','.join([str(lid) for lid in predicted])
]) + '\n')
if __name__ == '__main__':
data_dir = "../data/"
size_file = data_dir + "Gowalla_data_size.txt"
check_in_file = data_dir + "Gowalla_checkins.txt"
train_file = data_dir + "Gowalla_train.txt"
tune_file = data_dir + "Gowalla_tune.txt"
test_file = data_dir + "Gowalla_test.txt"
social_file = data_dir + "Gowalla_social_relations.txt"
poi_file = data_dir + "Gowalla_poi_coos.txt"
user_num, poi_num = open(size_file, 'r').readlines()[0].strip('\n').split()
user_num, poi_num = int(user_num), int(poi_num)
top_k = 100
FCF = FriendBasedCF()
KDE = KernelDensityEstimation()
main()