https://github.com/allenjack/HGN

Hierarchical Gating Networks for Sequential Recommendation
https://github.com/allenjack/HGN

Last synced: 3 months ago
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Hierarchical Gating Networks for Sequential Recommendation

• Host: GitHub
• URL: https://github.com/allenjack/HGN
• Owner: allenjack
• License: mit
• Created: 2019-05-15T01:10:38.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2021-10-19T09:17:42.000Z (about 3 years ago)
• Last Synced: 2024-06-24T05:51:54.615Z (5 months ago)
• Language: Jupyter Notebook
• Size: 19.9 MB
• Stars: 137
• Watchers: 5
• Forks: 24
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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• StarryDivineSky - allenjack/HGN - level以及instance-level的，通过Hierarchical Gating来处理group-level的信息,item-item的乘积来捕捉商品之间的关系。 (其他_推荐系统 / 网络服务_其他)

README

        
# The HGN model for Sequential Recommendation

The implementation of the paper:

*Chen Ma, Peng Kang, and Xue Liu, "**Hierarchical Gating Networks for Sequential Recommendation**", in the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (**KDD 2019**)* 

Arxiv: https://arxiv.org/abs/1906.09217

**Please cite our paper if you use our code. Thanks!**

Author: Chen Ma ([email protected])

**Feel free to send me an email if you have any questions.**

**Bibtex**

```

@inproceedings{DBLP:conf/kdd/MaKL19,

  author    = {Chen Ma and

               Peng Kang and

               Xue Liu},

  title     = {Hierarchical Gating Networks for Sequential Recommendation},

  booktitle = {{KDD}},

  pages     = {825--833},

  publisher = {{ACM}},

  year      = {2019}

}

```

## Environments

- python 3.6

- PyTorch (version: 1.0.0)

- numpy (version: 1.15.0)

- scipy (version: 1.1.0)

- sklearn (version: 0.19.1)

## Dataset

In our experiments, the *movielens-20M* dataset is from https://grouplens.org/datasets/movielens/20m/, the *Amazon-CDs* and *Amazon-Books* datasets are from http://jmcauley.ucsd.edu/data/amazon/, the *GoodReads-Children* and *GoodReads-Comics* datasets are from https://sites.google.com/eng.ucsd.edu/ucsdbookgraph/home. (If you need the data after preprocessing, please ~~send me an email~~ check this [Google Drive link](https://drive.google.com/file/d/1fPTpXFActieWBjowJpAF0YzctFHALCp3/view?usp=sharing)).

The ```XXX_tem_sequences.pkl``` file is a list of lists that stores the inner item id of each user in a chronological order, e.g., ```user_records[0]=[item_id0, item_id1, item_id2,...]```.

The ```XXX_user_mapping.pkl``` file is a list that maps the user inner id to its original id, e.g., ```user_mapping[0]=A2SUAM1J3GNN3B```.

The ```XXX_item_mapping.pkl``` file is similar to ```XXX_user_mapping.pkl```.

## Example to run the code

Data preprocessing:

The code for data preprocessing is put in the ```/preprocessing``` folder. ```Amazon_CDs.ipynb``` provides an example on how to transform the raw data into the ```.pickle``` files that used in our program.

Train and evaluate the model (you are strongly recommended to run the program on a machine with GPU):

```

python run.py

```

## Another implementation

Kindly thank [Liwei Wu](https://github.com/wuliwei9278) for another implementation with a different evaluation strategy: https://github.com/wuliwei9278/HGN_baseline.

## Acknowledgment

The sequence segmentation (interactions.py) is heavily built on [Spotlight](https://github.com/maciejkula/spotlight). Thanks for the amazing work.