def init_user_fea(self):
"""Initialize user feature for VBCAR model."""
if "user_fea_type" in self.config["dataset"]:
fea_type = self.config["dataset"]["user_fea_type"]
else:
fea_type = "random"
print(
"init user featrue for dataset:",
self.config["dataset"]["dataset"],
" type:",
fea_type,
)
if fea_type == "random":
self.user_feature = get_random_rep(self.n_users, self.random_dim)
else:
print(
"[ERROR]: CANNOT support other feature type, use 'random' user featrue instead!"
)
self.user_feature = get_random_rep(self.n_users, self.random_dim)
def init_item_fea(self):
"""Initialize item feature."""
config = self.config
if "item_fea_type" in config["dataset"]:
fea_type = config["dataset"]["item_fea_type"]
else:
fea_type = "random"
data_str = config["dataset"]["dataset"]
print(
"Loading item feature for dataset:",
data_str,
" type:",
fea_type,
)
if fea_type == "random":
self.item_feature = get_random_rep(self.n_items, self.random_dim)
elif fea_type == "one_hot":
item_fea_dic = load_item_fea_dic(config, fea_type="one_hot")
self.item_feature = np.array([
item_fea_dic[self.id2item[k]] for k in np.arange(self.n_items)
])
elif fea_type == "word2vec":
item_fea_dic = load_item_fea_dic(config, fea_type="word2vec")
self.item_feature = np.array([
item_fea_dic[self.id2item[k]] for k in np.arange(self.n_items)
])
elif fea_type == "bert":
item_fea_dic = load_item_fea_dic(config, fea_type="bert")
self.item_feature = np.array([
item_fea_dic[self.id2item[k]] for k in np.arange(self.n_items)
])
elif fea_type == "random_one_hot" or fea_type == "one_hot_random":
rand_item_fea = get_random_rep(self.n_items, 512)
item_fea_dic = load_item_fea_dic(config, fea_type="one_hot")
item_fea_list = np.array([
item_fea_dic[self.id2item[k]] for k in np.arange(self.n_items)
])
self.item_feature = np.concatenate((item_fea_list, rand_item_fea),
axis=1)
elif fea_type == "random_cate" or fea_type == "cate_random":
rand_item_fea = get_random_rep(self.n_items, 512)
item_fea_dic = load_item_fea_dic(config, fea_type="cate")
item_fea_list = np.array([
item_fea_dic[self.id2item[k]] for k in np.arange(self.n_items)
])
self.item_feature = np.concatenate((item_fea_list, rand_item_fea),
axis=1)
elif fea_type == "random_word2vec" or fea_type == "word2vec_random":
rand_item_fea = get_random_rep(self.n_items, 512)
item_fea_dic = load_item_fea_dic(config, fea_type="word2vec")
item_fea_list = np.array([
item_fea_dic[self.id2item[k]] for k in np.arange(self.n_items)
])
self.item_feature = np.concatenate((item_fea_list, rand_item_fea),
axis=1)
elif fea_type == "random_bert" or fea_type == "bert_random":
rand_item_fea = get_random_rep(self.n_items, 512)
item_fea_dic = load_item_fea_dic(config, fea_type="bert")
item_fea_list = np.array([
item_fea_dic[self.id2item[k]] for k in np.arange(self.n_items)
])
self.item_feature = np.concatenate((item_fea_list, rand_item_fea),
axis=1)
elif fea_type == "word2vec_one_hot" or fea_type == "one_hot_word2vec":
item_fea_dic1 = load_item_fea_dic(config, fea_type="one_hot")
item_fea_dic2 = load_item_fea_dic(config, fea_type="word2vec")
item_fea_list1 = np.array([
item_fea_dic1[self.id2item[k]] for k in np.arange(self.n_items)
])
item_fea_list2 = np.array([
item_fea_dic2[self.id2item[k]] for k in np.arange(self.n_items)
])
self.item_feature = np.concatenate(
(item_fea_list1, item_fea_list2), axis=1)
elif (fea_type == "word2vec_one_hot_random"
or fea_type == "random_one_hot_word2vec"
or fea_type == "one_hot_random_word2vec"
or fea_type == "random_word2vec_one_hot"):
rand_item_fea = get_random_rep(self.n_items, 512)
item_fea_dic1 = load_item_fea_dic(config, fea_type="one_hot")
item_fea_dic2 = load_item_fea_dic(config, fea_type="word2vec")
item_fea_list1 = np.array([
item_fea_dic1[self.id2item[k]] for k in np.arange(self.n_items)
])
item_fea_list2 = np.array([
item_fea_dic2[self.id2item[k]] for k in np.arange(self.n_items)
])
self.item_feature = np.concatenate(
(item_fea_list1, item_fea_list2, rand_item_fea), axis=1)
elif (fea_type == "word2vec_one_hot_bert"
or fea_type == "bert_one_hot_word2vec"
or fea_type == "one_hot_bert_word2vec"):
item_fea_dic1 = load_item_fea_dic(config, fea_type="one_hot")
item_fea_dic2 = load_item_fea_dic(config, fea_type="word2vec")
item_fea_dic3 = load_item_fea_dic(config, fea_type="bert")
item_fea_list1 = np.array([
item_fea_dic1[self.id2item[k]] for k in np.arange(self.n_items)
])
item_fea_list2 = np.array([
item_fea_dic2[self.id2item[k]] for k in np.arange(self.n_items)
])
item_fea_list3 = np.array([
item_fea_dic3[self.id2item[k]] for k in np.arange(self.n_items)
])
self.item_feature = np.concatenate(
(item_fea_list1, item_fea_list2, item_fea_list3), axis=1)
elif (fea_type == "random_word2vec_one_hot_bert"
or fea_type == "bert_one_hot_word2vec_random"
or fea_type == "one_hot_random_bert_word2vec"
or fea_type == "one_hot_bert_random_word2vec"
or fea_type == "one_hot_word2vec_bert_random"
or fea_type == "random_one_hot_word2vec_bert"):
rand_item_fea = get_random_rep(self.n_items, 512)
item_fea_dic1 = load_item_fea_dic(config, fea_type="one_hot")
item_fea_dic2 = load_item_fea_dic(config, fea_type="word2vec")
item_fea_dic3 = load_item_fea_dic(config, fea_type="bert")
item_fea_list1 = np.array([
item_fea_dic1[self.id2item[k]] for k in np.arange(self.n_items)
])
item_fea_list2 = np.array([
item_fea_dic2[self.id2item[k]] for k in np.arange(self.n_items)
])
item_fea_list3 = np.array([
item_fea_dic3[self.id2item[k]] for k in np.arange(self.n_items)
])
self.item_feature = np.concatenate((item_fea_list1, item_fea_list2,
item_fea_list3, rand_item_fea),
axis=1)
else:
print(
"[ERROR]: CANNOT support feature type {}! intialize with random feature"
.format(fea_type))
self.item_feature = get_random_rep(self.n_items, self.random_dim)