$ pip install -r requirements.txt
$ sudo apt install wget unzip

$ bash download.sh

$ python evaluate.py --data 100k --prov cosine --sensitive popularity
$ python evaluate.py --data 100k --prov bpr --sensitive popularity
$ python evaluate.py --data 100k --prov cosine --sensitive old
$ python evaluate.py --data 100k --prov bpr --sensitive old
$ python evaluate.py --data hetrec --prov bpr --sensitive popularity
$ python evaluate.py --data home --prov bpr --sensitive popularity
$ python evaluate_adult.py

$ python evaluate.py --data 100k --prov cosine --sensitive popularity --split 7 --block 0
$ python evaluate.py --data 100k --prov cosine --sensitive popularity --split 7 --block 1
$ python evaluate.py --data 100k --prov cosine --sensitive popularity --split 7 --block 2
$ python evaluate.py --data 100k --prov cosine --sensitive popularity --split 7 --block 3
$ python evaluate.py --data 100k --prov cosine --sensitive popularity --split 7 --block 4
$ python evaluate.py --data 100k --prov cosine --sensitive popularity --split 7 --block 5
$ python evaluate.py --data 100k --prov cosine --sensitive popularity --split 7 --block 6
$ python summary.py 7

@inproceedings{sato2022retrieving,
  author    = {Ryoma Sato},
  title     = {Private Recommender Systems: How Can Users Build Their Own Fair Recommender Systems without Log Data?},
  booktitle = {Proceedings of the 2022 {SIAM} International Conference on Data Mining, {SDM}},
  year      = {2022},
}