https://github.com/shenweichen/dsin

Code for the IJCAI'19 paper "Deep Session Interest Network for Click-Through Rate Prediction"
https://github.com/shenweichen/dsin

advertising-dataset click-through-rate ctr deep-learning dien din dsin ijcai recommender-system session-based-recommendation-system

Last synced: 17 days ago
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Code for the IJCAI'19 paper "Deep Session Interest Network for Click-Through Rate Prediction"

• Host: GitHub
• URL: https://github.com/shenweichen/dsin
• Owner: shenweichen
• License: apache-2.0
• Created: 2019-05-17T09:44:07.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2023-05-23T04:07:02.000Z (over 1 year ago)
• Last Synced: 2024-10-14T21:10:30.584Z (29 days ago)
• Topics: advertising-dataset, click-through-rate, ctr, deep-learning, dien, din, dsin, ijcai, recommender-system, session-based-recommendation-system
• Language: Python
• Homepage: https://arxiv.org/abs/1905.06482
• Size: 29.3 KB
• Stars: 432
• Watchers: 14
• Forks: 132
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# 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](https://github.com/649435349) , 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)

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## 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](https://tianchi.aliyun.com/dataset/dataDetail?dataId=56)

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](./LICENSE) for additional details.