Implementation of Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction using tensorflow
- Python 2.x
- Tensorflow 1.4
First we need to prepare data.
- You can get the raw Amazon data prepared
sh prepare_amazon.sh
- Because getting and processing the data is time consuming，we had processed Amazon data and upload it for you.
sh prepare_ready_data.sh
First download Taobao Data to get "UserBehavior.csv.zip", then execute the following command.
sh prepare_taobao.sh
usage: train_book.py|train_taobao.py [-h] [-p TRAIN|TEST] [--random_seed RANDOM_SEED]
[--model_type MODEL_TYPE] [--memory_size MEMORY_SIZE]
[--mem_induction MEM_INDUCTION]
[--util_reg UTIL_REG]
The example for DNN
python script/train_book.py -p train --random_seed 19 --model_type DNN
python script/train_book.py -p test --random_seed 19 --model_type DNN
The model below had been supported:
- DNN
- PNN
- DIN
- GRU4REC
- ARNN
- RUM
- DIEN
- DIEN_with_neg
You can train MIMN with different parameter setting:
- MIMN Basic
python script/train_taobao.py -p train --random_seed 19 --model_type MIMN --memory_size 4 --mem_induction 0 --util_reg 0
- MIMN with Memory Utilization Regularization
python script/train_taobao.py -p train --random_seed 19 --model_type MIMN --memory_size 4 --mem_induction 0 --util_reg 1
- MIMN with Memory Utilization Regularization and Memory Induction Unit
python script/train_taobao.py -p train --random_seed 19 --model_type MIMN --memory_size 4 --mem_induction 1 --util_reg 1
- MIMN with Auxiliary Loss
python script/train_taobao.py -p train --random_seed 19 --model_type MIMN_with_neg --memory_size 4 --mem_induction 0 --util_reg 0
If you want to train Amazon Data, you just need replace above train_taobao.py to train_book.py
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