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)
class Hybrid003AlphaRecommender(BaseRecommender):
"""Hybrid003AlphaRecommender recommender"""
RECOMMENDER_NAME = "Hybrid003AlphaRecommender"
def __init__(self, data: DataObject):
super(Hybrid003AlphaRecommender, self).__init__(data.urm_train)
self.data = data
self.poco_warm_recommender = Hybrid101AlphaRecommender(data)
self.quasi_warm_recommender = Hybrid102AlphaRecommender(data)
# self.warm_recommender = ItemKNNCFRecommender(data.urm_train)
# self.warm_recommender = Hybrid105AlphaRecommender(data)
self.warm_recommender = RP3betaRecommender(data.urm_train)
self.warm_7_recommender = ItemKNNCFRecommender(data.urm_train)
self.warm_8_recommender = Hybrid108AlphaRecommender(data)
self.warm_9_recommender = Hybrid109AlphaRecommender(data)
self.cold_recommender = Hybrid100AlphaRecommender(data)
def fit(self):
self.cold_recommender.fit()
self.poco_warm_recommender.fit()
self.quasi_warm_recommender.fit()
# self.warm_recommender.fit(topK=30, shrink=30, feature_weighting="none", similarity="jaccard")
self.warm_recommender.fit(topK=20, alpha=0.16, beta=0.24)
self.warm_7_recommender.fit(topK=12,
shrink=15,
feature_weighting="none",
similarity="jaccard")
self.warm_8_recommender.fit()
self.warm_9_recommender.fit()
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.data.ids_ultra_cold_users:
return self.cold_recommender.recommend(user_id_array,
cutoff=cutoff)
elif user_id_array in self.data.urm_train_users_by_type[0][1]:
return self.cold_recommender.recommend(user_id_array,
cutoff=cutoff)
elif user_id_array in self.data.urm_train_users_by_type[1][1]:
return self.poco_warm_recommender.recommend(user_id_array,
cutoff=cutoff)
elif user_id_array in self.data.urm_train_users_by_type[2][1]:
return self.quasi_warm_recommender.recommend(user_id_array,
cutoff=cutoff)
elif user_id_array in self.data.urm_train_users_by_type[3][1]:
return self.warm_recommender.recommend(user_id_array,
cutoff=cutoff)
elif user_id_array in self.data.urm_train_users_by_type[4][1]:
return self.warm_recommender.recommend(user_id_array,
cutoff=cutoff)
elif user_id_array in self.data.urm_train_users_by_type[5][1]:
return self.warm_recommender.recommend(user_id_array,
cutoff=cutoff)
elif user_id_array in self.data.urm_train_users_by_type[6][1]:
return self.warm_recommender.recommend(user_id_array,
cutoff=cutoff)
elif user_id_array in self.data.urm_train_users_by_type[7][1]:
return self.warm_7_recommender.recommend(user_id_array,
cutoff=cutoff)
elif user_id_array in self.data.urm_train_users_by_type[8][1]:
return self.warm_8_recommender.recommend(user_id_array,
cutoff=cutoff)
elif user_id_array in self.data.urm_train_users_by_type[9][1]:
return self.warm_9_recommender.recommend(user_id_array,
cutoff=cutoff)
elif user_id_array in self.data.ids_cold_user:
return self.cold_recommender.recommend(user_id_array,
cutoff=cutoff)