def __init__(self, data: DataObject):
super(Hybrid100AlphaRecommender, self).__init__(data.urm_train)
self.data = data
self.rec1 = UserKNNCBFRecommender(data.ucm_all, data.urm_train)
self.rec2 = MatrixFactorization_AsySVD_Cython(data.urm_train)
self.random_seed = data.random_seed
try:
self.rec1.load_model(
"stored_recommenders/user_cbf/",
f"{self.random_seed}_topK=5000_shrink=5_feature_weighting=TF-IDF_similarity=euclidean"
)
except:
self.rec1.fit(topK=5000,
shrink=5,
feature_weighting="TF-IDF",
similarity="euclidean")
self.rec1.save_model(
"stored_recommenders/user_cbf/",
f"{self.random_seed}_topK=5000_shrink=5_feature_weighting=TF-IDF_similarity=euclidean"
)
self.rec2.load_model("Hybrid", "AsySVD")
cold = data.ids_cold_user
train_cold = data.urm_train_users_by_type[0][1]
# if train_cold.shape[0] > 0:
# target_users = np.append(cold, train_cold)
# else:
# target_users = cold
target_users = data.ids_user
self.hybrid_rec = Hybrid1CXAlphaRecommender(
data,
recommenders=[self.rec1, self.rec2],
recommended_users=target_users,
max_cutoff=30)
def trial_inter(data):
rec = UserKNNCBFRecommender(UCM=data.ucm_interaction, URM_train=data.urm_train)
rec.fit()
for n, users, description in data.urm_train_users_by_type:
eval, map = MyEvaluator.evaluate_algorithm(data.urm_test, users, rec, at=10, remove_top=0)
print(f"\t {description},\t {eval}")
print(MyEvaluator.evaluate_algorithm(data.urm_test, data.ids_warm_user, rec))
class Hybrid001AlphaRecommender(BaseRecommender):
"""Hybrid001AlphaRecommender recommender"""
RECOMMENDER_NAME = "Hybrid001AlphaRecommender"
def __init__(self, URM_train, UCM, cold_users, warm_users):
super(Hybrid001AlphaRecommender, self).__init__(URM_train)
self.warm_recommender = ItemKNNCFRecommender(URM_train)
self.cold_recommender = UserKNNCBFRecommender(UCM, URM_train)
self.cold_users = cold_users
self.warm_users = warm_users
def fit(self):
self.warm_recommender.fit(topK=12, shrink=16)
self.cold_recommender.fit(topK=11000, shrink=2)
def recommend(self,
user_id_array,
cutoff=None,
remove_seen_flag=True,
items_to_compute=None,
remove_top_pop_flag=False,
remove_CustomItems_flag=False,
return_scores=False):
if user_id_array in self.warm_users:
return self.warm_recommender.recommend(user_id_array,
cutoff=cutoff)
if user_id_array in self.cold_users:
return self.cold_recommender.recommend(user_id_array,
cutoff=cutoff)
def __init__(self, urm_train, eurm=False):
super(HybridGenRecommender, self).__init__(urm_train)
self.data_folder = Path(__file__).parent.parent.absolute()
self.eurm = eurm
self.num_users = urm_train.shape[0]
data = DataManager()
urm_train = check_matrix(urm_train.copy(), 'csr')
icm_price, icm_asset, icm_sub, icm_all = data.get_icm()
ucm_age, ucm_region, ucm_all = data.get_ucm()
recommender_1 = ItemKNNCBFRecommender(urm_train, icm_all)
recommender_1.fit(shrink=40, topK=20, feature_weighting='BM25')
# recommender_2 = UserKNNCBFRecommender(urm_train, ucm_all)
# recommender_2.fit(shrink=500, topK=1600, normalize=True)
recommender_2 = UserKNNCBFRecommender(urm_train, ucm_all)
recommender_2.fit(shrink=1777,
topK=1998,
similarity='tversky',
feature_weighting='BM25',
tversky_alpha=0.1604953616,
tversky_beta=0.9862348646)
self.recommender_1 = recommender_1
self.recommender_2 = recommender_2
def single_test(urm_train, urm_test, urm_valid):
evaluator_valid = EvaluatorHoldout(urm_valid, cutoff_list=[10])
evaluator_test = EvaluatorHoldout(urm_test, cutoff_list=[10])
recommender = UserKNNCBFRecommender(urm_train, ucm_all)
recommender.fit(shrink=1777,
topK=1998,
similarity='tversky',
feature_weighting='BM25',
tversky_alpha=0.1604953616,
tversky_beta=0.9862348646)
result, str_result = evaluator_test.evaluateRecommender(recommender)
# result, str_result = evaluator_valid.evaluateRecommender(recommender)
# res[num_test] = result[10]['MAP']
return result[10]['MAP']
def __init__(self, urm_train):
super(HybridZeroRecommender, self).__init__(urm_train)
urm_train = check_matrix(urm_train.copy(), 'csr')
data = DataManager()
ucm_age, ucm_region, ucm_all = data.get_ucm()
recommender_1 = TopPop(urm_train)
recommender_1.fit()
#
recommender_2 = UserKNNCBFRecommender(urm_train, ucm_all)
recommender_2.fit(shrink=500, topK=1600, similarity='tversky')
self.recommender_1 = recommender_1
self.recommender_2 = recommender_2
class Hybrid000AlphaRecommender(BaseRecommender):
"""Hybrid000AlphaRecommender recommender"""
RECOMMENDER_NAME = "Hybrid000AlphaRecommender"
def __init__(self, URM_train, UCM, cold_users, warm_users):
super(Hybrid000AlphaRecommender, self).__init__(URM_train)
self.warm_recommender = SLIM_BPR_Cython(URM_train)
self.cold_recommender = UserKNNCBFRecommender(UCM, URM_train)
self.cold_users = cold_users
self.warm_users = warm_users
def fit(self,
random_seed=42,
epochs=500,
topK=14000,
shrink=2,
lambda_i=0.0,
lambda_j=0.0):
self.warm_recommender.fit(epochs=epochs,
lambda_i=lambda_i,
lambda_j=lambda_j,
random_seed=random_seed)
self.cold_recommender.fit(topK=topK,
shrink=shrink,
feature_weighting="BM25")
def recommend(self,
user_id_array,
cutoff=None,
remove_seen_flag=True,
items_to_compute=None,
remove_top_pop_flag=False,
remove_CustomItems_flag=False,
return_scores=False):
if user_id_array in self.warm_users:
return self.warm_recommender.recommend(user_id_array,
cutoff=cutoff)
if user_id_array in self.cold_users:
return self.cold_recommender.recommend(user_id_array,
cutoff=cutoff)
def __init__(self, data: DataObject):
super(Hybrid100AlphaRecommender, self).__init__(data.urm_train)
self.data = data
self.rec1 = UserKNNCBFRecommender(data.ucm_all, data.urm_train)
self.rec2 = TopPop(data.urm_train)
self.rec1.fit(topK=5000,
shrink=5,
feature_weighting="TF-IDF",
similarity="euclidean")
self.rec2.fit()
cold = data.ids_cold_user
train_cold = data.urm_train_users_by_type[0][1]
if train_cold.shape[0] > 0:
target_users = np.append(cold, train_cold)
else:
target_users = cold
self.target_users = target_users
self.hybrid_rec = Hybrid1CXAlphaRecommender(
data,
recommenders=[self.rec1, self.rec2],
recommended_users=target_users,
max_cutoff=20)
class Hybrid100AlphaRecommender(BaseRecommender):
"""Hybrid100AlphaRecommender recommender"""
RECOMMENDER_NAME = "Hybrid100AlphaRecommender"
def __init__(self, data: DataObject):
super(Hybrid100AlphaRecommender, self).__init__(data.urm_train)
self.data = data
self.rec1 = UserKNNCBFRecommender(data.ucm_all, data.urm_train)
self.rec2 = MatrixFactorization_AsySVD_Cython(data.urm_train)
self.random_seed = data.random_seed
try:
self.rec1.load_model(
"stored_recommenders/user_cbf/",
f"{self.random_seed}_topK=5000_shrink=5_feature_weighting=TF-IDF_similarity=euclidean"
)
except:
self.rec1.fit(topK=5000,
shrink=5,
feature_weighting="TF-IDF",
similarity="euclidean")
self.rec1.save_model(
"stored_recommenders/user_cbf/",
f"{self.random_seed}_topK=5000_shrink=5_feature_weighting=TF-IDF_similarity=euclidean"
)
self.rec2.load_model("Hybrid", "AsySVD")
cold = data.ids_cold_user
train_cold = data.urm_train_users_by_type[0][1]
# if train_cold.shape[0] > 0:
# target_users = np.append(cold, train_cold)
# else:
# target_users = cold
target_users = data.ids_user
self.hybrid_rec = Hybrid1CXAlphaRecommender(
data,
recommenders=[self.rec1, self.rec2],
recommended_users=target_users,
max_cutoff=30)
def fit(self):
weights = [
[
0.7609041954308008, 0.27339862890657357, 0.3144084232425596,
0.28678002559795207, 0.3170292366001356, 0.24147239220026245,
0.14009508172349497, 0.15649320080818696, 0.16743130156581199,
0.18707405544524536, 0.19350324993686288, 0.21987953364962454,
0.2233212596556791, 0.19217500924897687, 0.19736284590367756,
0.2100228313521805, 0.18526521644738797, 0.19299238737073587,
0.12892725272565625, 0.12007519721696268, 0.08916255488946315,
0.09565831116101862, 0.08921835911617365, 0.09575012944298913,
0.06061814295035001, 0.06788462944119182, 0.037165855793027704,
0.04105324668609332, 0.023576935696646744, 0.0
],
[
1.0, 0.23662052508936088, 0.2225837530904237,
0.2536834803952595, 0.2917360024545484, 0.20844068794221393,
0.11251972620359912, 0.10895216412067295, 0.11738478513380732,
0.10458147363449094, 0.10786698616858342, 0.11776718227577632,
0.10415827006216491, 0.10285083017703549, 0.11514393572529961,
0.07647362838535736, 0.08649824797408665, 0.0906695206756817,
0.07494150129954372, 0.06680458369333378, 0.06642942053751508,
0.06459816503682635, 0.07336768255019717, 0.030660821531648116,
0.027881710157031087, 0.019316732889272565,
0.02139829161607211, 0.024608035358482924,
0.016094537787800765, 0.010481732651884517
]
]
# weights = [[20.75, 12., 15.25, 12.25, 9.75, 12.25, 8.5, 5., 8.5,
# 9.75, 8.25, 5., 4.75, 7.25, 6.75, 6.25, 8., 8.,
# 9.5, 7.25, 8.75, 8.25, 6.75, 10.25, 12.25, 8., 8.5,
# 10., 7.75, 6.5],
# [11., 13., 10., 5.25, 7.5, 10., 9.25, 9., 4.,
# 12.5, 10.75, 11.75, 14., 8.5, 6.5, 6.5, 3.75, 1.75,
# 6.25, 7.25, 6.25, 8.5, 1.75, 7., 5.5, 6.25, 5.5,
# 4.25, 3., 4.5]]
self.hybrid_rec.weights = weights
def recommend(self,
user_id_array,
cutoff=None,
remove_seen_flag=True,
items_to_compute=None,
remove_top_pop_flag=False,
remove_CustomItems_flag=False,
return_scores=False):
return self.hybrid_rec.recommend(user_id_array=user_id_array,
cutoff=cutoff)
def __init__(self, urm_train, eurm=False):
super(HybridNormOrigRecommender, self).__init__(urm_train)
self.data_folder = Path(__file__).parent.parent.absolute()
self.eurm = eurm
self.num_users = urm_train.shape[0]
data = DataManager()
urm_train = check_matrix(urm_train.copy(), 'csr')
icm_price, icm_asset, icm_sub, icm_all = data.get_icm()
ucm_age, ucm_region, ucm_all = data.get_ucm()
recommender_1 = ItemKNNCBFRecommender(urm_train, icm_all)
recommender_1.fit(shrink=40, topK=20, feature_weighting='BM25')
recommender_7 = UserKNNCBFRecommender(urm_train, ucm_all)
recommender_7.fit(shrink=1777,
topK=1998,
similarity='tversky',
feature_weighting='BM25',
tversky_alpha=0.1604953616,
tversky_beta=0.9862348646)
# recommender_1 = HybridGenRecommender(urm_train, eurm=self.eurm)
# recommender_1.fit()
# recommender_2 = ItemKNNCFRecommender(urm_train)
# recommender_2.fit(shrink=30, topK=20)
recommender_2 = ItemKNNCFRecommender(urm_train)
recommender_2.fit(topK=5,
shrink=500,
feature_weighting='BM25',
similarity='tversky',
normalize=False,
tversky_alpha=0.0,
tversky_beta=1.0)
recommender_3 = UserKNNCFRecommender(urm_train)
recommender_3.fit(shrink=2, topK=600, normalize=True)
# recommender_3 = UserKNNCFRecommender(urm_train)
# recommender_3.fit(topK=697, shrink=1000, feature_weighting='TF-IDF', similarity='tversky', normalize=False,
# tversky_alpha=1.0, tversky_beta=1.0)
recommender_4 = RP3betaRecommender(urm_train)
recommender_4.fit(topK=16,
alpha=0.03374950051351756,
beta=0.24087176329409027,
normalize_similarity=False)
recommender_5 = SLIM_BPR_Cython(urm_train)
recommender_5.fit(lambda_i=0.0926694015,
lambda_j=0.001697250,
learning_rate=0.002391,
epochs=65,
topK=200)
recommender_6 = ALSRecommender(urm_train)
recommender_6.fit(alpha=5, iterations=40, reg=0.3)
self.recommender_1 = recommender_1
self.recommender_2 = recommender_2
self.recommender_3 = recommender_3
self.recommender_4 = recommender_4
self.recommender_5 = recommender_5
self.recommender_6 = recommender_6
self.recommender_7 = recommender_7
if self.eurm:
if Path(self.data_folder / 'Data/uicm_orig_sparse.npz').is_file():
print("Previous uicm_sparse found")
self.score_matrix_1 = sps.load_npz(self.data_folder /
'Data/uicm_sparse.npz')
else:
print("uicm_sparse not found, create new one...")
self.score_matrix_1 = self.recommender_1._compute_item_matrix_score(
np.arange(self.num_users))
sps.save_npz(self.data_folder / 'Data/uicm_orig_sparse.npz',
self.score_matrix_1)
self.score_matrix_2 = self.recommender_2._compute_item_matrix_score(
np.arange(self.num_users))
self.score_matrix_3 = self.recommender_3._compute_item_matrix_score(
np.arange(self.num_users))
self.score_matrix_4 = self.recommender_4._compute_item_matrix_score(
np.arange(self.num_users))
self.score_matrix_5 = self.recommender_5._compute_item_matrix_score(
np.arange(self.num_users))
self.score_matrix_6 = self.recommender_6._compute_item_score(
np.arange(self.num_users))
self.score_matrix_1 = normalize(self.score_matrix_2,
norm='max',
axis=1)
self.score_matrix_2 = normalize(self.score_matrix_2,
norm='max',
axis=1)
self.score_matrix_3 = normalize(self.score_matrix_3,
norm='max',
axis=1)
self.score_matrix_4 = normalize(self.score_matrix_4,
norm='max',
axis=1)
self.score_matrix_5 = normalize(self.score_matrix_5,
norm='max',
axis=1)
self.score_matrix_6 = normalize(self.score_matrix_6,
norm='max',
axis=1)
#
# if result_dict[10]["MAP"] > best_res:
# best_alpha = alpha
# best_res = result_dict[10]["MAP"]
#
# print('The alpha is {}'.format(best_alpha))
#
# pyplot.plot(x_tick, MAP_per_k_valid)
# pyplot.ylabel('MAP_valid')
# pyplot.xlabel('alpha')
# pyplot.show()
#
# recommender = UserKNNCBFRecommender(urm_train, ucm_all)
# recommender.fit(shrink=500, topK=1600, similarity='tversky')
recommender = UserKNNCBFRecommender(urm_train, ucm_all)
recommender.fit(shrink=1777,
topK=1998,
similarity='tversky',
feature_weighting='BM25',
tversky_alpha=0.1604953616,
tversky_beta=0.9862348646)
# recommender = UserKNNCBFRecommender(urm_train, ucm_all)
# recommender.fit(shrink=979, topK=1677, similarity='jaccard')
#
# recommender = UserKNNCBFRecommender(urm_train, ucm_all)
# recommender.fit(shrink=1000, topK=1700, similarity='tanimoto', normalize=False)
# recommender_3 = UserKNNCFRecommender(urm_train)
# recommender_3.fit(shrink=2, topK=600, normalize=True)
def __init__(self, URM_train, UCM, cold_users, warm_users):
super(Hybrid001AlphaRecommender, self).__init__(URM_train)
self.warm_recommender = ItemKNNCFRecommender(URM_train)
self.cold_recommender = UserKNNCBFRecommender(UCM, URM_train)
self.cold_users = cold_users
self.warm_users = warm_users
alpha=1e-6,
beta=0.2,
min_rating=0)
recommenderUserKNN = UserKNNCFRecommender(URM_all)
recommenderUserKNN.fit(topK=550,
shrink=0,
similarity='jaccard',
normalize=True)
recommenderIALS = IALSRecommender(URM_all)
# recommenderIALS.fit(epochs=200, alpha=1, reg=1e-4)
# recommenderIALS.save_model('model/', file_name='IALS_1_200_max')
recommenderIALS.load_model('model/', file_name='IALS_1_200_max')
recommenderUserCBF = UserKNNCBFRecommender(URM_all, UCM_all)
recommenderUserCBF.fit(topK=3000,
shrink=10,
similarity='cosine',
normalize=True)
hybridRecommender = ItemKNNScoresHybridRecommender(
URM_all, itemKNNCF, recommenderELASTIC, recommenderCB,
recommenderBetaGRAPH, recommenderUserKNN, recommenderCYTHON,
recommenderIALS)
hybridRecommender_semiCold = ItemKNNScoresHybridRecommender(
URM_all, itemKNNCF, recommenderELASTIC, recommenderCB,
recommenderBetaGRAPH, recommenderUserKNN, recommenderCYTHON,
recommenderIALS)
class Hybrid100AlphaRecommender(BaseRecommender):
"""Hybrid100AlphaRecommender recommender"""
RECOMMENDER_NAME = "Hybrid100AlphaRecommender"
def __init__(self, data: DataObject):
super(Hybrid100AlphaRecommender, self).__init__(data.urm_train)
self.data = data
self.rec1 = UserKNNCBFRecommender(data.ucm_all, data.urm_train)
self.rec2 = TopPop(data.urm_train)
self.rec1.fit(topK=5000,
shrink=5,
feature_weighting="TF-IDF",
similarity="euclidean")
self.rec2.fit()
cold = data.ids_cold_user
train_cold = data.urm_train_users_by_type[0][1]
if train_cold.shape[0] > 0:
target_users = np.append(cold, train_cold)
else:
target_users = cold
self.target_users = target_users
self.hybrid_rec = Hybrid1CXAlphaRecommender(
data,
recommenders=[self.rec1, self.rec2],
recommended_users=target_users,
max_cutoff=20)
def fit(self):
weights = [
[
1.0, 0.6821568252468901, 0.6849024969656253,
0.5278884115166208, 0.5256279588345396, 0.3523944402333277,
0.35917072524518867, 0.3781483902477164, 0.35494012640675526,
0.2569761057765945, 0.2686708563615972, 0.26047695765786827,
0.18963922521929752, 0.19796884202620682, 0.21062192323921447,
0.21086230330374983, 0.20645211181144751, 0.2051545899987307,
0.195630375727827, 0.16433006044601312, 0.15898416104687418,
0.13710408424057147, 0.14535409877804073, 0.14093706131152708,
0.13925057012140438, 0.10897116615151631, 0.08690310920352357,
0.05452137761633189, 0.039626490480693964, 0.038884188246590415
],
[
0.12184895015208339, 0.10412792995227525, 0.11245816434845729,
0.09237425432552643, 0.07790937226904884, 0.08414212205057275,
0.06899821248751226, 0.07451806948651324, 0.06080223143736178,
0.05923095707738565, 0.0639694336435765, 0.06890447942434409,
0.06474046677091937, 0.0672866612162612, 0.06627995889505314,
0.06170959010489348, 0.05576908741213118, 0.05516680106058056,
0.04913346628366165, 0.042346032068023606, 0.04268540340456829,
0.03738588109631485, 0.03801703741074155, 0.03634930102905383,
0.027148968808256298, 0.023744038897221154,
0.019516056544059187, 0.009815556133702266,
0.0038914891192522562, 0.0034155098002848494
]
]
self.hybrid_rec.weights = weights
def recommend(self,
user_id_array,
cutoff=None,
remove_seen_flag=True,
items_to_compute=None,
remove_top_pop_flag=False,
remove_CustomItems_flag=False,
return_scores=False):
if user_id_array in self.target_users:
return self.hybrid_rec.recommend(user_id_array=user_id_array,
cutoff=cutoff)
else:
return []
def __init__(self, URM_train, UCM, cold_users, warm_users):
super(Hybrid000AlphaRecommender, self).__init__(URM_train)
self.warm_recommender = SLIM_BPR_Cython(URM_train)
self.cold_recommender = UserKNNCBFRecommender(UCM, URM_train)
self.cold_users = cold_users
self.warm_users = warm_users
def __init__(self, urm_train, eurm=False):
super(HybridGen2Recommender, self).__init__(urm_train)
self.eurm = eurm
self.data_folder = Path(__file__).parent.parent.absolute()
self.num_users = urm_train.shape[0]
urm_train = check_matrix(urm_train.copy(), 'csr')
data = DataManager()
if Path(self.data_folder /
'Data/icm_sparse.npz').is_file() and self.eurm:
print("Previous icm_sparse found")
else:
_, _, _, icm_all = data.get_icm()
args = {
'topK': 6,
'shrink': 5,
'feature_weighting': 'TF-IDF',
'similarity': 'cosine',
'normalize': False
}
recommender_1 = ItemKNNCBFRecommender(urm_train, icm_all)
recommender_1.fit(**args)
self.recommender_1 = recommender_1
if Path(self.data_folder /
'Data/ucm_sparse.npz').is_file() and self.eurm:
print("Previous ucm_sparse found")
else:
ucm_age, ucm_region, ucm_all = data.get_ucm()
recommender_2 = UserKNNCBFRecommender(urm_train, ucm_all)
recommender_2.fit(shrink=1777,
topK=1998,
similarity='tversky',
feature_weighting='BM25',
tversky_alpha=0.1604953616,
tversky_beta=0.9862348646)
self.recommender_2 = recommender_2
if self.eurm:
beta = 0.6
if Path(self.data_folder / 'Data/icm_sparse.npz').is_file():
self.score_matrix_1 = sps.load_npz(self.data_folder /
'Data/icm_sparse.npz')
else:
print("icm_sparse not found, create new one...")
self.score_matrix_1 = self.recommender_1._compute_item_matrix_score(
np.arange(self.num_users))
user_score_matrix_1 = normalize(self.score_matrix_1,
norm='max',
axis=1)
item_score_matrix_1 = normalize(self.score_matrix_1.tocsc(),
norm='max',
axis=0)
self.score_matrix_1 = item_score_matrix_1 * beta + user_score_matrix_1.tocsr(
) * (1 - beta)
sps.save_npz(self.data_folder / 'Data/icm_sparse.npz',
self.score_matrix_1)
if Path(self.data_folder / 'Data/ucm_sparse.npz').is_file():
self.score_matrix_2 = sps.load_npz(self.data_folder /
'Data/ucm_sparse.npz')
else:
print("ucm_sparse not found, create new one...")
self.score_matrix_2 = self.recommender_2._compute_item_matrix_score(
np.arange(self.num_users))
user_score_matrix_2 = normalize(self.score_matrix_2,
norm='max',
axis=1)
item_score_matrix_2 = normalize(self.score_matrix_2.tocsc(),
norm='max',
axis=0)
self.score_matrix_2 = item_score_matrix_2 * beta + user_score_matrix_2.tocsr(
) * (1 - beta)
sps.save_npz(self.data_folder / 'Data/ucm_sparse.npz',
self.score_matrix_2)
# TODO: Edit here
# TODO: If you edit here, remember to edit at the end too
# Insert the mix of recommender systems
rec1 = UserKNNCFRecommender(datas[j].urm_train)
rec1.fit(shrink=1000,
topK=1000,
similarity="cosine",
feature_weighting="TF-IDF")
rec2 = RP3betaRecommender(datas[j].urm_train)
rec2.fit(topK=5000, alpha=0.35, beta=0.025, implicit=True)
rec3 = ItemKNNCFRecommender(datas[j].urm_train)
rec3.fit(topK=200,
shrink=1000,
similarity="jaccard",
feature_weighting="TF-IDF")
rec4 = UserKNNCBFRecommender(datas[j].ucm_all, datas[j].urm_train)
rec4.fit(topK=5000,
shrink=5,
feature_weighting="TF-IDF",
similarity="euclidean")
rec5 = ItemKNNCBFRecommender(datas[j].urm_train,
datas[j].icm_all_augmented)
rec5.fit(topK=100,
shrink=1,
feature_weighting="TF-IDF",
similarity="cosine")
base_recommenders.append([rec1, rec2, rec3, rec4, rec5])
# TODO: Edit here
# Insert the type of users to test the recommenders
n, t_users, description = datas[j].urm_train_users_by_type[1]
