用于顺序推荐的分层门控网络_Jupyter Notebook_Python_下载.zip

# 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 (allenmc1230@gmail.com) **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.