首先，安装Torch-RecHub：
bash
pip install torch-rechub然后，使用以下代码进行推荐系统模型的训练：
精排（CTR预测）
python
from torch_rechub.models.ranking import DeepFM
from torch_rechub.trainers import CTRTrainer
from torch_rechub.utils.data import DataGenerator
dg = DataGenerator(x, y)
train_dataloader, val_dataloader, test_dataloader = dg.generate_dataloader(split_ratio=[0.7, 0.1], batch_size=256)
model = DeepFM(deep_features=deep_features, fm_features=fm_features, mlp_params={"dims": [256, 128], "dropout": 0.2, "activation": "relu"})
ctr_trainer = CTRTrainer(model)
ctr_trainer.fit(train_dataloader, val_dataloader)
auc = ctr_trainer.evaluate(ctr_trainer.model, test_dataloader)多任务排序
python
from torch_rechub.models.multi_task import SharedBottom, ESMM, MMOE, PLE, AITM
from torch_rechub.trainers import MTLTrainer
task_types = ["classification", "classification"]
model = MMOE(features, task_types, 8, expert_params={"dims": [32,16]}, tower_params_list=[{"dims": [32, 16]}, {"dims": [32, 16]}])
mtl_trainer = MTLTrainer(model)
mtl_trainer.fit(train_dataloader, val_dataloader)
auc = ctr_trainer.evaluate(ctr_trainer.model, test_dataloader)召回模型
python
from torch_rechub.models.matching import DSSM
from torch_rechub.trainers import MatchTrainer
from torch_rechub.utils.data import MatchDataGenerator
dg = MatchDataGenerator(x y)
train_dl, test_dl, item_dl = dg.generate_dataloader(test_user, all_item, batch_size=256)
model = DSSM(user_features, item_features, temperature=0.02,
user_params={
"dims": [256, 128, 64],
"activation": 'prelu',
},
item_params={
"dims": [256, 128, 64],
"activation": 'prelu',
})
match_trainer = MatchTrainer(model)
match_trainer.fit(train_dl)更多详细示例和模型实现，请参考 API 参考 部分。