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.