A PyTorch implementation of Ranking Distillation:

Ranking Distillation: Learning Compact Ranking Models With High Performance for Recommender System, Jiaxi Tang and Ke Wang , KDD '18

• Python 2 or 3
• PyTorch v0.4+
• Numpy
• SciPy

1. Run python train_caser.py with d=50 to get the performance of student model.

   When finished, we will have the model saved in folder checkpoints/

1. Run python train_caser.py with d=100 to get the performance of teacher model.

   When finished, we will have the model saved in folder checkpoints/

1. Run python train_caser.py with d=100 to get the well-trained teacher model.

   When finished, we will have the teacher model saved in folder checkpoints/

   (you can also skip this step, as there is one in the checkpoint/gowalla-caser-dim=100.pth.tar)

2. Run python distill_caser.py with d=50 and teacher_model_path pointed to the teacher model.

• d is set to 50 for student model and 100 for teacher model.

• All other the hyper-parameters (e.g., nh, nv, ac_conv, ac_fc) are set by grid-search.

  Please check this repo for more information and definations of these hyper-parameters.

• teacher_model_path: path to teacher's model checkpoint.
• teacher_topk_path: (optional) path to teacher's top-K ranking cache for each training query.
• teach_alpha: hyperparameter for balancing ranking loss and distillation loss.
• K: length of teacher's exemplary ranking.
• lamda: hyperparameter for tuning the sharpness of position importance weight.
• mu: hyperparameter for tuning the sharpness of ranking discrepancy weight.
• dynamic_samples: number of samples used for estimating student's rank.
• dynamic_start_epoch: number of iteration to start using hybrid of two different weights.

If you use this Caser in your paper, please cite the paper:

@inproceedings{tang2018ranking,
  title={Ranking Distillation: Learning Compact Ranking Models With High Performance for Recommender System},
  author={Tang, Jiaxi and Wang, Ke},
  booktitle={ACM SIGKDD International Conference on Knowledge Discovery & Data Mining},
  year={2018}
}

This project (utils.py, interactions.py, etc.) is heavily built on Spotlight. Thanks Maciej Kula for his great work.