ENSFM

This is our implementation of the paper:

Chong Chen, Min Zhang, Weizhi Ma, Yiqun Liu and Shaoping Ma. 2020. Efficient Non-Sampling Factorization Machines for Optimal Context-Aware Recommendation. In TheWebConf'20.

Please cite our TheWebConf'20 paper if you use our codes. Thanks!

@inproceedings{chen2020efficient,
  title={Efficient Non-Sampling Factorization Machines for Optimal Context-Aware Recommendation},
  author={Chen, Chong and Zhang, Min and Ma, Weizhi and Liu, Yiqun and Ma, Shaoping},
  booktitle={Proceedings of The Web Conference},
  year={2020},
}

Author: Chong Chen (cstchenc@163.com)

Baselines

For FM, NFM, ONCF and CFM, we use the implementations released in https://github.com/chenboability/CFM.

For Frappe and Last.fm datasets, the results of FM, DeepFM, NFM, ONCF, and CFM are the same as those reported in CFM: Convolutional Factorization Machines for Context-Aware Recommendation. since we share exactly the same data splits and experimental settings.

Environments

• python
• Tensorflow
• numpy
• pandas

Example to run the codes

Train and evaluate the model:

python ENSFM.py

Suggestions for parameters

Two important parameters need to be tuned for different datasets, which are:

parser.add_argument('--dropout', type=float, default=1,
                        help='dropout keep_prob')
parser.add_argument('--negative_weight', type=float, default=0.5,
                        help='weight of non-observed data')

Specifically, we suggest to tune "negative_weight" among [0.001,0.005,0.01,0.02,0.05,0.1,0.2,0.5]. Generally, this parameter is related to the sparsity of dataset. If the dataset is more sparse, then a small value of negative_weight may lead to a better performance.

Generally, the performance of our ENSFM is much better than existing state-of-the-art FM models like NFM, DeepFM, and CFM on Top-K recommendation task. You can also contact us if you can not tune the parameters properly.

First Update Date: May 19, 2020