The implementation of the paper:
Pengfei Wang, Yu Fan, Long Xia, Wayne Xin Zhao, Shaozhang Niu and Jimmy Huang , "KERL: A Knowledge-Guided Reinforcement Learning Model for Sequential Recommendation", SIGIR 2020
Please cite our paper if you use our code. Thanks!
Author: Yu Fan (fanyubupt@gmail.com)
- python 3.6
- PyTorch (version: 1.0.0)
- numpy (version: 1.15.0)
- scipy (version: 1.1.0)
- sklearn (version: 0.19.1)
In our experiments, the Amazon-Beauty Amazon-CDs and Amazon-Books datasets are from http://jmcauley.ucsd.edu/data/amazon/, the LastFM dataset is from http://www.cp.jku.at/datasets/LFM-1b/.
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.
The beauty dataset is in the file as an example.
The KB embedding is trained by transE based on projects of THUNLP(https://github.com/thunlp). OpenKE(https://github.com/thunlp/OpenKE) is their main project, you can find nearly all methods related here, including all TransX model used in the paper.
Reproducing the results reported in our paper, please run the code as follows:
python runRL.py