CoCheLab

Code for the Content Caching algorithm in edge caching.

Environment configuration

git clone https://github.com/DarriusL/CacheLab.git
cd CacheLab
conda env create -f cachelab_dev.yml
conda activate cachelab_dev
git update-index --assume-unchanged config/lab_cfg.json
git update-index --assume-unchanged config/data_process_cfg.json

Framework file structure

CacheLab
├── .gitignore
├── cache
│	└── logger
│		└── logger.log
├── cachelab_env.yml
├── config
│	├── caser/.
│	├── CFG_README.md
│	├── cl4srec/.
│	├── data_process_cfg.json
│	├── duo4srec/.
│	├── ec4srec/.
│	├── egpc/.
│	├── lab_cfg.json
│	└── psac/.
├── data
│	├── augmentation.py
│	├── datasets
│	│	├── meta
│	│	│	├── appliances
│	│	│	│	├── Appliances.json
│	│	│	│	└── Appliances_lite.json
│	│	│	├── beauty
│	│	│	│	├── All_Beauty.json
│	│	│	│	└── All_Beauty_lite.json
│	│	│	├── kindle
│	│	│	│	├── Kindle_Store.json
│	│	│	│	└── Kindle_Store_lite.json
│	│	│	├── ml-1m
│	│	│	│	├── movies.dat
│	│	│	│	├── ratings.dat
│	│	│	│	├── README
│	│	│	│	└── users.dat
│	│	│	└── music
│	│	│		├── Digital_Music.json
│	│	│		└── Digital_Music_lite.json
│	│	└── process
│	│		├── complete
│	│		│	├── All_Beauty.data
│	│		│	├── Appliances.data
│	│		│	├── appliances_devide_25.data
│	│		│	├── Digital_Music.data
│	│		│	├── kindle_devide_25.data
│	│		│	├── Kindle_Store.data
│	│		│	├── ml.data
│	│		│	├── ml_devide_55.data
│	│		│	└── music_devide_25.data
│	│		└── lite
│	│			├── All_Beauty_lite.data
│	│			├── Appliances_lite.data
│	│			├── Digital_Music_lite.data
│	│			├── Kindle_Store_lite.data
│	│			├── ml.data
│	│			└── music_devide_25.data
│	├── generator.py
│	├── processor.py
│	├── saved/.
│	└── __init__.py
|
├── executor.py
├── lib
│	├── callback.py
│	├── glb_var.py
│	├── graph_util.py
│	├── json_util.py
│	└── util.py
|
├── model
│	├── attnet.py
│	├── cnnnet.py
│	└── framework
│		├── caser.py
│		├── cl4srec.py
│		├── duo4srec.py
│		├── ec4srec.py
│		├── egpc.py
│		└── psac.py
├── README.md
└── Room
	├── officer.py
	├── work.py
	└── __init__.py

Dataset acquisition:

ML-1M

Amazon review lite

Amazon review

Algorithms that have been implemented and algorithms that will be supported soon:

CL-based:

CL4SRec[1]
Duo4SRec(DuoSRec)[2]
EC4SRec[3]
EGPC

DL-based:

PSAC_gen[4]
Caser[5]

Command

usage

usage: executor.py [-h] [--data_process DATA_PROCESS] [--config CONFIG] [--saved_config SAVED_CONFIG] [--mode MODE]

options

-h, --help            show this help message and exit
--data_process DATA_PROCESS, -dp DATA_PROCESS
                    type for data process(None/lite/complete)
--config CONFIG, -cfg CONFIG
                    config for run
--saved_config SAVED_CONFIG, -sc SAVED_CONFIG
                    path for saved config to test
--mode MODE           train/test/train_and_test

For configuration files, see:./config/CFG_README.md

quick start

process dataset

You need to configure the data processing configuration file yourself:@./config/data_process_cfg.json

python executor.py --data_process=True

CL4SRec

python executor.py -cfg='./config/cl4srec/cl4srec_ml1m.json' --mode=train
python executor.py -cfg='./config/cl4srec/cl4srec_ml1m.json' --mode=train_and_test
python executor.py -sc='./data/saved/cl4srec/ml1m/pre_64_2048_2_2/config.json' --mode=test

python executor.py -cfg='./config/cl4srec/cl4srec_appliances.json' --mode=train
python executor.py -cfg='./config/cl4srec/cl4srec_appliances.json' --mode=train_and_test
python executor.py -sc='./data/saved/cl4srec/appliances/pre_64_2048_2_2/config.json' --mode=test

python executor.py -cfg='./config/cl4srec/cl4srec_music.json' --mode=train
python executor.py -cfg='./config/cl4srec/cl4srec_music.json' --mode=train_and_test
python executor.py -sc='./data/saved/cl4srec/music/pre_64_2048_2_2/config.json' --mode=test

Duo4SRec

python executor.py -cfg='./config/duo4srec/duo4srec_ml1m.json' --mode=train
python executor.py -cfg='./config/duo4srec/duo4srec_ml1m.json' --mode=train_and_test
python executor.py -sc='./data/saved/duo4srec/ml1m/pre_64_2048_2_2/config.json' --mode=test

python executor.py -cfg='./config/duo4srec/duo4srec_appliances.json' --mode=train
python executor.py -cfg='./config/duo4srec/duo4srec_appliances.json' --mode=train_and_test
python executor.py -sc='./data/saved/duo4srec/appliances/pre_64_2048_2_2/config.json' --mode=test

python executor.py -cfg='./config/duo4srec/duo4srec_music.json' --mode=train
python executor.py -cfg='./config/duo4srec/duo4srec_music.json' --mode=train_and_test
python executor.py -sc='./data/saved/duo4srec/music/pre_64_2048_2_2/config.json' --mode=test

EC4SRec

python executor.py -cfg='./config/ec4srec/ec4srec_ml1m.json' --mode=train
python executor.py -cfg='./config/ec4srec/ec4srec_ml1m.json' --mode=train_and_test
python executor.py -sc='./data/saved/ec4srec/ml1m/pre_64_2048_2_2/config.json' --mode=test

python executor.py -cfg='./config/ec4srec/ec4srec_appliances.json' --mode=train
python executor.py -cfg='./config/ec4srec/ec4srec_appliances.json' --mode=train_and_test
python executor.py -sc='./data/saved/ec4srec/appliances/pre_64_2048_2_2/config.json' --mode=test

python executor.py -cfg='./config/ec4srec/ec4srec_music.json' --mode=train
python executor.py -cfg='./config/ec4srec/ec4srec_music.json' --mode=train_and_test
python executor.py -sc='./data/saved/ec4srec/music/pre_64_2048_2_2/config.json' --mode=test

EGPC

python executor.py -cfg='./config/egpc/egpc_ml1m.json' --mode=train
python executor.py -cfg='./config/egpc/egpc_ml1m.json' --mode=train_and_test
python executor.py -sc='./data/saved/egpc/ml1m/pre_64_2048_2_2/config.json' --mode=test

python executor.py -cfg='./config/egpc/egpc_appliances.json' --mode=train
python executor.py -cfg='./config/egpc/egpc_appliances.json' --mode=train_and_test
python executor.py -sc='./data/saved/egpc/appliances/pre_64_2048_2_2/config.json' --mode=test

python executor.py -cfg='./config/egpc/egpc_music.json' --mode=train
python executor.py -cfg='./config/egpc/egpc_music.json' --mode=train_and_test
python executor.py -sc='./data/saved/egpc/music/pre_64_2048_2_2/config.json' --mode=test

Caser

python executor.py -cfg='./config/caser/caser_ml1m.json' --mode=train
python executor.py -cfg='./config/caser/caser_ml1m.json' --mode=train_and_test
python executor.py -sc='./data/saved/caser/ml1m/64_8/config.json' --mode=test

python executor.py -cfg='./config/caser/caser_appliances.json' --mode=train
python executor.py -cfg='./config/caser/caser_appliances.json' --mode=train_and_test
python executor.py -sc='./data/saved/caser/appliances/64_8/config.json' --mode=test

python executor.py -cfg='./config/caser/caser_music.json' --mode=train
python executor.py -cfg='./config/caser/caser_music.json' --mode=train_and_test
python executor.py -sc='./data/saved/caser/music/64_8/config.json' --mode=test

PSAC

python executor.py -cfg='./config/psac/psac_gen_ml1m.json' --mode=train
python executor.py -cfg='./config/psac/psac_gen_ml1m.json' --mode=train_and_test
python executor.py -sc='./data/saved/psac_gen/ml1m/64_8/config.json' --mode=test

python executor.py -cfg='./config/psac/psac_gen_appliances.json' --mode=train
python executor.py -cfg='./config/psac/psac_gen_appliances.json' --mode=train_and_test
python executor.py -sc='./data/saved/psac_gen/appliances/64_8/config.json' --mode=test

python executor.py -cfg='./config/psac/psac_gen_music.json' --mode=train
python executor.py -cfg='./config/psac/psac_gen_music.json' --mode=train_and_test
python executor.py -sc='./data/saved/psac_gen/music/64_8/config.json' --mode=test

Refrence

Xie X, Sun F, Liu Z, et al. Contrastive learning for sequential recommendation[C]//2022 IEEE 38th international conference on data engineering (ICDE). IEEE, 2022: 1259-1273.
Qiu R, Huang Z, Yin H, et al. Contrastive learning for representation degeneration problem in sequential recommendation[C]//Proceedings of the fifteenth ACM international conference on web search and data mining. 2022: 813-823.
Wang L, Lim E P, Liu Z, et al. Explanation guided contrastive learning for sequential recommendation[C]//Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 2022: 2017-2027.
Zhang Y, Li Y, Wang R, et al. PSAC: Proactive sequence-aware content caching via deep learning at the network edge[J]. IEEE Transactions on Network Science and Engineering, 2020, 7(4): 2145-2154.
Tang J, Wang K. Personalized top-n sequential recommendation via convolutional sequence embedding[C]//Proceedings of the eleventh ACM international conference on web search and data mining. 2018: 565-573.

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Repository files navigation

CoCheLab

Environment configuration

Framework file structure

Command

usage

options

quick start

Refrence

About

Uh oh!

Releases

Packages

Uh oh!

Contributors

Uh oh!

Languages

Name		Name	Last commit message	Last commit date
Latest commit History 74 Commits
Room		Room
config		config
data		data
lib		lib
model		model
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
cachelab_env.yml		cachelab_env.yml
executor.py		executor.py

Folders and files

Latest commit

History

Repository files navigation

CoCheLab

Environment configuration

Framework file structure

Command

usage

options

quick start

Refrence

About

Resources

License

Uh oh!

Stars

Watchers

Forks

Releases

Packages 0

Uh oh!

Contributors

Uh oh!

Languages

Packages