Running New Dataset

Here, we present how to use a new dataset in our RecBole.

Convert to Atomic Files

If the user use the collected datasets, she can choose one of the following ways:

1. Download the converted atomic files from Google Drive or Baidu Wangpan (Password: e272).

2. Find the converting script from RecDatasets, and transform them to atomic files.

If the user use the other datasets, she should format the data according to the format of the atomic files.

For the dataset of ml-1m, the converting file is:

ml-1m.inter

user_id:token	item_id:token	rating:float	timestamp:float
1	1193	5	978300760
1	661	3	978302109

ml-1m.user

user_id:token	age:token	gender:token	occupation:token	zip_code:token
1	1	F	10	48067
2	56	M	16	70072

ml-1m.item

item_id:token	movie_title:token_seq	release_year:token	genre:token_seq
1	Toy Story	1995	Animation Children’s Comedy
2	Jumanji	1995	Adventure Children’s Fantasy

Local Path

Name of atomic files, name of dir that containing atomic files and config['dataset'] should be the same.

config['data_path'] should be the parent dir of the dir that containing atomic files.

For example:

~/xxx/yyy/ml-1m/
├── ml-1m.inter
├── ml-1m.item
├── ml-1m.kg
├── ml-1m.link
└── ml-1m.user

data_path: ~/xxx/yyy/
dataset: ml-1m

Convert to Dataset

Here, we present how to convert atomic files into Dataset.

Suppose we use ml-1m to train BPR.

According to the dataset information, the user should set the dataset information and filtering parameters in the configuration file ml-1m.yaml. For example, we conduct 10-core filtering, removing the ratings which are smaller than 3, the time of the record should be earlier than 97830000, and we only load inter data.

USER_ID_FIELD: user_id
ITEM_ID_FIELD: item_id
RATING_FIELD: rating
TIME_FIELD: timestamp

load_col:
    inter: [user_id, item_id, rating, timestamp]

min_user_inter_num: 10
min_item_inter_num: 10
lowest_val:
    rating: 3
    timestamp: 97830000

from recbole.config import Config
from recbole.data import create_dataset, data_preparation

if __name__ == '__main__':
    config = Config(model='BPR', dataset='ml-1m', config_file_list=['ml-1m.yaml'])
    dataset = create_dataset(config)

Convert to Dataloader

Here, we present how to convert Dataset into Dataloader.

We firstly set the parameters in the configuration file ml-1m.yaml. We leverage random ordering + ratio-based splitting and full ranking with all item candidates, the splitting ratio is set as 8:1:1.

...

eval_setting: RO_RS,full
split_ratio: [0.8,0.1,0.1]

from recbole.config import Config
from recbole.data import create_dataset, data_preparation


if __name__ == '__main__':

    ...

    train_data, valid_data, test_data = data_preparation(config, dataset)