CC-CC

This is our implementation for the paper:

Shaoyun Shi, Min Zhang, Xinxing Yu, Yongfeng Zhang, Bin Hao, Yiqun Liu and Shaoping Ma. 2019. Adaptive Feature Sampling for Recommendation with Missing Content Feature Values In CIKM'19.

Please cite our paper if you use our codes. Thanks!

Author: Shaoyun Shi (shisy13 AT gmail.com)

@inproceedings{shi2019adaptive,
  title={Adaptive Feature Sampling for Recommendation with Missing Content Feature Values},
  author={Shaoyun Shi, Min Zhang, Xinxing Yu, Yongfeng Zhang, Bin Hao, Yiqun Liu and Shaoping Ma},
  booktitle={Proceedings of the 28th ACM International Conference on Information and Knowledge Management},
  pages={1451--1460},
  year={2019},
  organization={ACM}
}

Environments

Python 3.6.7

Packages: See in requirements.txt

tensorflow==1.4.1
pandas==0.23.4
scipy==1.1.0
tqdm==4.28.1
numpy==1.15.4
scikit_learn==0.21.3

Datasets

The processed datasets is in ./dataset.

ml-100k: The origin dataset can be found here.
Zhihu: The origin dataset can be found here.

Example to run the codes

# CC-CC with Adaptive-Feature-Sampling
> cd CC-CC/src/
> python main.py --model_name FSCCCC --dataset ml100k-r-i30-u30-f10 --optimizer Adagrad --l2 1e-4 --cs_ratio 0.2 --fs_ratio 0.2 --fs_mode afs --lr 5e-2 --random_seed 2018 --gpu 1

THUIR/CC-CC

CC-CC

Environments

Datasets

Example to run the codes