Python recommender-system Deep learning
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Description

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

254 Stars 93 Forks Apache License 2.0 4 Commits 10 Opened issues

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Deep Session Interest Network for Click-Through Rate Prediction

Experiment code on Advertising Dataset of paper Deep Session Interest Network for Click-Through Rate Prediction(https://arxiv.org/abs/1905.06482)

Yufei Feng , Fuyu Lv, Weichen Shen and Menghan Wang and Fei Sun and Yu Zhu and Keping Yang.

In Proceedings of 28th International Joint Conference on Artificial Intelligence (IJCAI 2019)


Operating environment

please use

pip install -r requirements.txt
to setup the operating environment in
python3.6
.

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(It may take a long time to sample negative samples.)
  2. run
    train_dien.py

Train DSIN model

  1. run
    2_gen_dsin_input.py
    ,generate input data
  2. run
    train_dsin.py
    > The loss of DSIN with
    bias_encoding=True
    may be NaN sometimes on Advertising Dataset and it remains a confusing problem since it never occurs in the production environment.We will work on it and also appreciate your help.

License

This project is licensed under the terms of the Apache-2 license. See LICENSE for additional details.

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