| batch_size | Model | max-AUC |
|---|---|---|
| 32 | Two-Tower | 0.877 |
| 32 | DIN(without Dice) | 0.893 |
- Python 3.6
- Numpy 1.18.5
- Pandas 1.1.3
- TensorFlow 2.3.1
wget -c http://snap.stanford.edu/data/amazon/productGraph/categoryFiles/reviews_Electronics_5.json.gz
gzip -d reviews_Electronics_5.json.gz
wget -c http://snap.stanford.edu/data/amazon/productGraph/categoryFiles/meta_Electronics.json.gz
gzip -d meta_Electronics.json.gz- Step 1: generate tfrecord dataset
python generate_tfrecord.pyor use spark to generate tfrecord
(I use zeppelin and code save in generate_tfrecord.scala)
- Step 2: training and evaluation
python main.pyyou need confirm tfrecord dataset path and param "data_gen_method"("spark" or "python")
- you can change Two-Tower model to DIN model in main.py's model_fn
docker pull tensorflow/serving
docker run -t --rm -p 8501:8501 \
-v xxx/saved_model/:/models/model \
-e MODEL_NAME=model \
tensorflow/serving &
curl localhost:8501/v1/models/model/metadata
curl -X POST -i 'http://localhost:8501/v1/models/model:predict' --data '
{
"signature_name": "predict",
"instances": [
{
"hist" :[48286, 12353, 49240, 26542, 47881, 44980, 36691, 54084, 9091, 60606, 24126, 42664, 8622, 2845, 28137, 21689, 9790, 3742, 46807, 25980, 416, 9358, 53047, 38638, 33061, 29065, 34054, 5383, 41023, 50676, 18606, 20428, 18530, 7092, 41364, 13874, 35322, 28369, 60598, 16183],
"hist_cate": [744, 183, 20, 504, 611, 401, 614, 42, 757, 171, 286, 643, 50, 737, 456, 797, 280, 245, 612, 596, 68, 265, 537, 633, 161, 45, 391, 124, 470, 310, 699, 104, 446, 398, 466, 318, 1, 758, 305, 687],
"item_id": [49240],
"pad_category_ids": [3]
}
]
}'
https://github.com/zhougr1993/DeepInterestNetwork.git
https://zhuanlan.zhihu.com/p/129700488
https://github.com/cdj0311/two_tower_recommendation_system.git
https://github.com/tensorflow/serving.git
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