JNSKR
This is our implementation of the paper:
Chong Chen, Min Zhang, Weizhi Ma, Yiqun Liu and Shaoping Ma. 2020. Jointly Non-Sampling Learning for Knowledge Graph Enhanced Recommendation. In SIGIR'20.
Please cite our SIGIR'20 paper if you use our codes. Thanks!
@inproceedings{chen2020jointly,
title={Jointly Non-Sampling Learning for Knowledge Graph Enhanced Recommendation},
author={Chen, Chong and Zhang, Min and Ma, Weizhi and Liu, Yiqun and Ma, Shaoping},
booktitle={Proceedings of SIGIR},
year={2020},
}
You also need to cite the KDD'19 paper if you use the datasets. Thanks!
@inproceedings{KGAT19,
author = {Xiang Wang and
Xiangnan He and
Yixin Cao and
Meng Liu and
Tat{-}Seng Chua},
title = {{KGAT:} Knowledge Graph Attention Network for Recommendation},
booktitle = {{KDD}},
pages = {950--958},
year = {2019}
}
Author: Chong Chen ([email protected])
Baselines and Datasets
We follow the previous work, KGAT, and you can get the detailed information about the baselines and datasets in https://github.com/xiangwang1223/knowledge_graph_attention_network.
Example to run the codes
Train and evaluate our model:
python main_JNSKR.py
Train and evaluate baselines:
python main_Baselines.py
Reproducibility
For Amazon dataset:
parser.add_argument('--dropout', type=float, default=[0.8,0.7],
help='dropout keep_prob')
parser.add_argument('--coefficient', type=float, default=[1.0, 0.01],
help='weight of multi-task')
parser.add_argument('--c0', type=float, default=300,
help='initial weight of non-observed data')
parser.add_argument('--c1', type=float, default=600,
help='initial weight of non-observed knowledge data')
For Yelp dataset:
parser.add_argument('--dropout', type=float, default=[0.9,0.7],
help='dropout keep_prob')
parser.add_argument('--coefficient', type=float, default=[1.0, 0.01],
help='weight of multi-task')
parser.add_argument('--c0', type=float, default=1000,
help='initial weight of non-observed data')
parser.add_argument('--c1', type=float, default=7000,
help='initial weight of non-observed knowledge data')
Suggestions for parameters
Several important parameters need to be tuned for different datasets, which are:
parser.add_argument('--dropout', type=float, default=[0.8,0.7],
help='dropout keep_prob')
parser.add_argument('--coefficient', type=float, default=[1.0, 0.01],
help='weight of multi-task')
parser.add_argument('--c0', type=float, default=300,
help='initial weight of non-observed data')
parser.add_argument('--c1', type=float, default=600,
help='initial weight of non-observed knowledge data')
Specifically, c0 and c1 determine the overall weight of non-observed data. The coefficient parameter determines the importance of different tasks in joint learning.
You can also contact us if you can not tune the parameters properly.