rahmanidashti / BeyondAccCalibration

Beyond-Accuracy Calibration

‌Personalised Beyond-accuracy Calibration in Recommendation

Dataset Preprocessing Pipeline

1. Downloading datasets raw files (datasets/DatasetName/raw)

2. Collecting required features and mapping IDs to range 0 - N (the number of users or items). To do this we provide each dataset a specific notebook (datasets/DatasetName/DatasetName_dataset.ipynb)

   • Output_Files_1: DatasetName/raw/ratings.csv and DatasetName/raw/poi.csv

   • Output_Files_2:: Here we create two file, one for the rating data which show a user's rating on an item, and another one is cat file. The cat file indicate the category of each item (datasets/DatasetName/raw/DatasetName_data_map.txt and datasets/DatasetName/raw/DatasetName_cat_map.txt)

3. category_checker.ipynb:

4. dataset.ipynb:

   • datasets/DatasetName/DS_NAME_data.txt
   • datasets/DatasetName/DS_NAME_cat.txt

5. GoogleDrive/0_dataset_in_use.ipnb:

   1. datasets/DatasetName/DS_Name_Train
   2. datasets/DatasetName/DS_Name_Test
   3. datasets/DatasetName/DS_Name_Category

Datasets

• ClothingFit: 5-core
• MovieLens1M: 10-core
• Yelp

Note

Will be update upon the acceptance of the paper.