8000 GitHub - hhh920406/DSIN: Code for the IJCAI'19 paper "Deep Session Interest Network for Click-Through Rate Prediction"
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Code for the IJCAI'19 paper "Deep Session Interest Network for Click-Through Rate Prediction"

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Deep Session Interest Network

Operating environment

  • python==3.6
  • tensorflow-gpu==1.4.0
  • deepctr==0.4.0.post0
  • numpy==1.15.1
  • pandas==0.22.0
  • scikit-learn==0.19.2
  • tqdm==4.19.5

Download dataset and preprocess

Download dataset

  1. Download Dataset Ad Display/Click Data on Taobao.com
  2. Extract the files into the raw_data directory

Data preprocessing

  1. run 0_gen_sampled_data.py, sample the data by user
  2. run 1_gen_sessions.py, generate historical session sequence for each user

Training and Evaluation

Train DIN model

  1. run 2_gen_din_input.py,generate input data
  2. run train_din.py

Train DIEN model

  1. run 2_gen_dien_input.py,generate input data(since the code is not optimized, this may take a long time to sample negative samples when setting DIEN_NEG_SAMPLING = True in config.py)
  2. run train_dien.py

Train DSIN model

  1. run 2_gen_dsin_input.py,generate input data
  2. run train_dsin.py

If the loss during the training is abnormally large, please restart the training process or change the seed.

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Code for the IJCAI'19 paper "Deep Session Interest Network for Click-Through Rate Prediction"

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