deem-data/recbole-models

RecBole Model Checkpoints

This repository contains 52 model checkpoint(s) for RecBole.

Repository Information

• Total Models: 52
• Format: PyTorch .pth files
• Framework: RecBole

Available Models

BPR

• model_BPR_seed_11_dataset_amazon_reviews_grocery_and_gourmet_food_retrain_checkpoint_idx_to_match_3_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_meat.pth
• model_BPR_seed_11_dataset_amazon_reviews_grocery_and_gourmet_food_unlearning_algorithm_fanchuan_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_alcohol_unlearn_epoch_150_retrain_checkpoint_idx_to_match_1.pth
• model_BPR_seed_11_dataset_amazon_reviews_grocery_and_gourmet_food_unlearning_algorithm_fanchuan_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_alcohol_unlearn_epoch_225_retrain_checkpoint_idx_to_match_2.pth
• model_BPR_seed_11_dataset_amazon_reviews_grocery_and_gourmet_food_unlearning_algorithm_fanchuan_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_alcohol_unlearn_epoch_75_retrain_checkpoint_idx_to_match_0.pth
• model_BPR_seed_11_dataset_amazon_reviews_grocery_and_gourmet_food_unlearning_algorithm_kookmin_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_alcohol_unlearn_epoch_150_retrain_checkpoint_idx_to_match_1.pth
• model_BPR_seed_11_dataset_amazon_reviews_grocery_and_gourmet_food_unlearning_algorithm_kookmin_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_alcohol_unlearn_epoch_225_retrain_checkpoint_idx_to_match_2.pth
• model_BPR_seed_11_dataset_amazon_reviews_grocery_and_gourmet_food_unlearning_algorithm_kookmin_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_alcohol_unlearn_epoch_75_retrain_checkpoint_idx_to_match_0.pth
• model_BPR_seed_11_dataset_amazon_reviews_grocery_and_gourmet_food_unlearning_algorithm_scif_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_alcohol_unlearn_epoch_150_retrain_checkpoint_idx_to_match_1.pth
• model_BPR_seed_11_dataset_amazon_reviews_grocery_and_gourmet_food_unlearning_algorithm_scif_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_alcohol_unlearn_epoch_225_retrain_checkpoint_idx_to_match_2.pth
• model_BPR_seed_11_dataset_amazon_reviews_grocery_and_gourmet_food_unlearning_algorithm_scif_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_alcohol_unlearn_epoch_75_retrain_checkpoint_idx_to_match_0.pth
• model_BPR_seed_11_dataset_amazon_reviews_grocery_and_gourmet_food_unlearning_algorithm_seif_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_alcohol_unlearn_epoch_150_retrain_checkpoint_idx_to_match_1.pth
• model_BPR_seed_11_dataset_amazon_reviews_grocery_and_gourmet_food_unlearning_algorithm_seif_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_alcohol_unlearn_epoch_225_retrain_checkpoint_idx_to_match_2.pth
• model_BPR_seed_11_dataset_amazon_reviews_grocery_and_gourmet_food_unlearning_algorithm_seif_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_alcohol_unlearn_epoch_75_retrain_checkpoint_idx_to_match_0.pth
• model_BPR_seed_11_dataset_goodreads_best.pth
• model_BPR_seed_11_dataset_goodreads_retrain_checkpoint_idx_to_match_3_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_health.pth
• model_BPR_seed_11_dataset_goodreads_unlearning_algorithm_fanchuan_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_health_unlearn_epoch_1450_retrain_checkpoint_idx_to_match_0.pth
• model_BPR_seed_11_dataset_goodreads_unlearning_algorithm_fanchuan_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_health_unlearn_epoch_2900_retrain_checkpoint_idx_to_match_1.pth
• model_BPR_seed_11_dataset_goodreads_unlearning_algorithm_fanchuan_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_health_unlearn_epoch_4350_retrain_checkpoint_idx_to_match_2.pth
• model_BPR_seed_11_dataset_goodreads_unlearning_algorithm_fanchuan_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_health_unlearn_epoch_5799_retrain_checkpoint_idx_to_match_3.pth
• model_BPR_seed_11_dataset_goodreads_unlearning_algorithm_kookmin_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_health_unlearn_epoch_1450_retrain_checkpoint_idx_to_match_0.pth
• model_BPR_seed_11_dataset_goodreads_unlearning_algorithm_kookmin_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_health_unlearn_epoch_2900_retrain_checkpoint_idx_to_match_1.pth
• model_BPR_seed_11_dataset_goodreads_unlearning_algorithm_kookmin_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_health_unlearn_epoch_4350_retrain_checkpoint_idx_to_match_2.pth
• model_BPR_seed_11_dataset_goodreads_unlearning_algorithm_kookmin_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_health_unlearn_epoch_5799_retrain_checkpoint_idx_to_match_3.pth
• model_BPR_seed_11_dataset_goodreads_unlearning_algorithm_scif_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_health_unlearn_epoch_1450_retrain_checkpoint_idx_to_match_0.pth
• model_BPR_seed_11_dataset_goodreads_unlearning_algorithm_scif_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_health_unlearn_epoch_2900_retrain_checkpoint_idx_to_match_1.pth
• model_BPR_seed_11_dataset_goodreads_unlearning_algorithm_scif_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_health_unlearn_epoch_4350_retrain_checkpoint_idx_to_match_2.pth
• model_BPR_seed_11_dataset_goodreads_unlearning_algorithm_scif_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_health_unlearn_epoch_5799_retrain_checkpoint_idx_to_match_3.pth
• model_BPR_seed_11_dataset_goodreads_unlearning_algorithm_seif_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_health_unlearn_epoch_1450_retrain_checkpoint_idx_to_match_0.pth
• model_BPR_seed_11_dataset_goodreads_unlearning_algorithm_seif_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_health_unlearn_epoch_2900_retrain_checkpoint_idx_to_match_1.pth
• model_BPR_seed_11_dataset_goodreads_unlearning_algorithm_seif_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_health_unlearn_epoch_4350_retrain_checkpoint_idx_to_match_2.pth
• model_BPR_seed_11_dataset_goodreads_unlearning_algorithm_seif_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_health_unlearn_epoch_5799_retrain_checkpoint_idx_to_match_3.pth
• model_BPR_seed_11_dataset_movielens_retrain_checkpoint_idx_to_match_3_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_violence.pth
• model_BPR_seed_11_dataset_movielens_unlearning_algorithm_fanchuan_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_health_unlearn_epoch_15_retrain_checkpoint_idx_to_match_0.pth
• model_BPR_seed_11_dataset_movielens_unlearning_algorithm_fanchuan_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_health_unlearn_epoch_30_retrain_checkpoint_idx_to_match_1.pth
• model_BPR_seed_11_dataset_movielens_unlearning_algorithm_fanchuan_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_health_unlearn_epoch_45_retrain_checkpoint_idx_to_match_2.pth
• model_BPR_seed_11_dataset_movielens_unlearning_algorithm_kookmin_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_health_unlearn_epoch_1462_retrain_checkpoint_idx_to_match_2.pth
• model_BPR_seed_11_dataset_movielens_unlearning_algorithm_kookmin_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_health_unlearn_epoch_15_retrain_checkpoint_idx_to_match_0.pth
• model_BPR_seed_11_dataset_movielens_unlearning_algorithm_kookmin_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_health_unlearn_epoch_30_retrain_checkpoint_idx_to_match_1.pth
• model_BPR_seed_11_dataset_movielens_unlearning_algorithm_kookmin_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_health_unlearn_epoch_45_retrain_checkpoint_idx_to_match_2.pth
• model_BPR_seed_11_dataset_movielens_unlearning_algorithm_kookmin_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_health_unlearn_epoch_487_retrain_checkpoint_idx_to_match_0.pth
• model_BPR_seed_11_dataset_movielens_unlearning_algorithm_kookmin_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_health_unlearn_epoch_60_retrain_checkpoint_idx_to_match_3.pth
• model_BPR_seed_11_dataset_movielens_unlearning_algorithm_kookmin_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_health_unlearn_epoch_975_retrain_checkpoint_idx_to_match_1.pth
• model_BPR_seed_11_dataset_movielens_unlearning_algorithm_scif_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_health_unlearn_epoch_1462_retrain_checkpoint_idx_to_match_2.pth
• model_BPR_seed_11_dataset_movielens_unlearning_algorithm_scif_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_health_unlearn_epoch_15_retrain_checkpoint_idx_to_match_0.pth
• model_BPR_seed_11_dataset_movielens_unlearning_algorithm_scif_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_health_unlearn_epoch_30_retrain_checkpoint_idx_to_match_1.pth
• model_BPR_seed_11_dataset_movielens_unlearning_algorithm_scif_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_health_unlearn_epoch_45_retrain_checkpoint_idx_to_match_2.pth
• model_BPR_seed_11_dataset_movielens_unlearning_algorithm_scif_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_health_unlearn_epoch_487_retrain_checkpoint_idx_to_match_0.pth
• model_BPR_seed_11_dataset_movielens_unlearning_algorithm_scif_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_health_unlearn_epoch_60_retrain_checkpoint_idx_to_match_3.pth
• model_BPR_seed_11_dataset_movielens_unlearning_algorithm_scif_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_health_unlearn_epoch_975_retrain_checkpoint_idx_to_match_1.pth
• model_BPR_seed_11_dataset_movielens_unlearning_algorithm_seif_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_health_unlearn_epoch_1462_retrain_checkpoint_idx_to_match_2.pth
• model_BPR_seed_11_dataset_movielens_unlearning_algorithm_seif_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_health_unlearn_epoch_15_retrain_checkpoint_idx_to_match_0.pth
• model_BPR_seed_11_dataset_movielens_unlearning_algorithm_seif_unlearning_fraction_0.0001_unlearning_sample_selection_method_sensitive_category_health_unlearn_epoch_30_retrain_checkpoint_idx_to_match_1.pth

Usage with RecBole

Method 1: Command Line

python run_recbole.py \
    --model YOUR_MODEL \
    --dataset YOUR_DATASET \
    --eval_only \
    --hf_model_path "hf://deem-data/recbole-models/FILENAME.pth"

Method 2: Python API

from recbole.utils import load_model_from_path
import torch

# Load checkpoint
checkpoint = load_model_from_path(
    model_path="hf://deem-data/recbole-models/FILENAME.pth",
    map_location="cpu"
)

# Load into your model
model.load_state_dict(checkpoint["state_dict"])

Method 3: Download All Models

from recbole.utils import HuggingFaceModelLoader

loader = HuggingFaceModelLoader()
repo_path = loader.download_repository(
    repo_id="deem-data/recbole-models",
    allow_patterns=["*.pth"]
)

print(f"All models downloaded to: {repo_path}")

Citation

If you use these models, please cite:

@inproceedings{recbole,
  title={RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms},
  author={Wayne Xin Zhao and Shanlei Mu and Yupeng Hou and Zihan Lin and Yushuo Chen and Xingyu Pan and Kaiyuan Li and Yujie Lu and Hui Wang and Changxin Tian and Yingqian Min and Zhichao Feng and Xinyan Fan and Xu Chen and Pengfei Wang and Wendi Ji and Yaliang Li and Xiaoling Wang and Ji-Rong Wen},
  booktitle={CIKM},
  year={2021}
}

License

MIT License (or specify your own)

Contact

For questions about these models, please contact: [your email or GitHub]