[ICDM'21] MASCOT: A Quantization Framework for Efficient Matrix Factorization in Recommender Systems

This repository provides a reference implementation of MASCOT as described in the following paper: MASCOT: A Quantization Framework for Efficient Matrix Factorization in Recommender Systems by Yunyong Ko, Jae-Seo Yu, Hong-Kyun Bae, Yongjun Park, Dongwon Lee, and Sang-Wook Kim, In IEEE International Conference on Data Mining (IEEE ICDM 2021).

This project is written in standard C++ and CUDA 10.2. It can be built by running Makefile in the source code directory.

Usage

Run executable file by:

./quantized_mf -i [train file] -y [test file] -o [output file] [options]

Where options are as follows:

-l : The number of epochs executed during training
-k : The dimensionality of latent space (64 or 128)
-b : Regularization parameter for users and items
-a : Initial learning rate
-d : Decay factor
-wg : The number of warps launched during update
-b : The number of threads per block
-ug : The number of user groups
-ig : The number of item groups
-r : Sampling ratio
-it : Error estimate period
-e : Error threshold
-rc : Whether to save reconstructed testset matrix
-v : MF version to run

It is recommended to tune the number of threads using -wg options to maximize the performance.
We used an RTX 2070 GPU for our experiments and set the number of warps to 2,048 (k = 128), 2,304 (k = 64)
Other parameter settings are described in the paper.

Datasets

In our experiments, we used four real-world datasets for training and testing.
In the case of ML10M and ML25M, we divide the training and test set 8:2 for 5-cross validation.
For Netflix and Yahoo!Music, we just use the provided training and test sets.

Test Pre-trained Models

We provide pre-trained models (.zip) and you can test it as follows:

./test_mf -i [pre-trained model file] -y [test file] -v [mf version]

Experimental results

First, We compare MASCOT and three state-of-the-art quantization methods in terms of training time and the model error. (RQ1~2)
Existing quantization methods are as follows :

• [ICLR '18] Mixed Precision Training (MPT)
• [ICML '20] Muti-Precision Policy Enforced Training (MuPPET)
• [CVPR '20] Adaptive Fixed Point (AFP)

In the next experiment, we verify the effectiveness of our strategies (m-quantization, g-switching) and optimization technique through an ablation study. (RQ3)
Finally, we evaluate the hyperparameter sensitivity of MASCOT and provide the best values for each hyperparameter, maximizing the performance improvement while maintaining the model errors low. (RQ4)

RQ1. Does MASCOT improve the training performance of MF models more than existing quantization methods?

RQ2. Does MASCOT provide the errors of MF models lower than existing quantization methods?

RQ3. How effective are the strategies and optimizations of MASCOT in improving the MF model training?

• Strategies of MASCOT (m-quantization, g-switching)

• Optimization technique

RQ4. How sensitive are the training performance and model error of MASCOT to its hyperparameters?

• Sampling ratio, error estimate period

• The number of groups

You can produce those result using following commands :

RQ 1~2 :

• MASCOT

  ./quantized_mf -i [train file] -y [test file] -o [output file] -wg 2048 -bl 128 -k 128 -l 50 -a 0.01 -d 0.1 -ug 100 -ig 100 -e 20 -s 0.05 -it 2 -v 1
  

• MPT

  ./quantized_mf -i [train file] -y [test file] -o [output file] -wg 2048 -bl 128 -k 128 -l 50 -a 0.01 -d 0.1 -v 4
  

• MuPPET

  ./quantized_mf -i [train file] -y [test file] -o [output file] -wg 2048 -bl 128 -k 128 -l 50 -a 0.01 -d 0.1 -s 0.05 -v 3
  

• AFP

  ./quantized_mf -i [train file] -y [test file] -o [output file] -wg 2048 -bl 128 -k 128 -l 50 -a 0.01 -d 0.1 -v 2
  

• FP32

  ./quantized_mf -i [train file] -y [test file] -o [output file] -wg 2048 -bl 128 -k 128 -l 50 -a 0.01 -d 0.1 -v 5
  

RQ 3:

• Strategies of MASCOT

  • FP32

  ./quantized_mf -i [train file] -y [test file] -o [output file] -wg 2048 -bl 128 -k 128 -l 50 -a 0.01 -d 0.1 -v 5
  

  • MASCOT-N1

  ./quantized_mf -i [train file] -y [test file] -o [output file] -wg 2048 -bl 128 -k 128 -l 50 -a 0.01 -d 0.1 -v 7
  

  • MASCOT-N2

  ./quantized_mf -i [train file] -y [test file] -o [output file] -wg 2048 -bl 128 -k 128 -l 50 -a 0.01 -d 0.1 -ug 100 -ig 100 -e [values] -s 0.05 -it 4 -v 8
  

  • MASCOT

  ./quantized_mf -i [train file] -y [test file] -o [output file] -wg 2048 -bl 128 -k 128 -l 50 -a 0.01 -d 0.1 -ug 100 -ig 100 -e 20 -s 0.05 -it 2 -v 1
  

• Optimization technique

  • MASCOT-naive

  ./quantized_mf -i [train file] -y [test file] -o [output file] -wg 2048 -bl 128 -k 128 -l 50 -a 0.01 -d 0.1 -ug 100 -ig 100 -e 20 -s 0.05 -it 2 -v 6
  

  • MASCOT-opt

  ./quantized_mf -i [train file] -y [test file] -o [output file] -wg 2048 -bl 128 -k 128 -l 50 -a 0.01 -d 0.1 -ug 100 -ig 100 -e 20 -s 0.05 -it 2 -v 1
  

RQ 4:

• Sampling ratio, error estimate period, # of groups
  • MASCOT

  ./quantized_mf -i [train file] -y [test file] -o [output file] -wg 2048 -bl 128 -k 128 -l 50 -a 0.01 -d 0.1 -ug 100 -ig 100 -e [values] -s [values] -it 2 -v 1
  

Citation

Please cite our paper if you have used the code in your work. You can use the following BibTex citation:

@inproceedings{ko2021mascot,
  title={MASCOT: A Quantization Framework for Efficient Matrix Factorization in Recommender Systems},
  author={Ko, Yunyong and Yu, Jae-Seo and Bae, Hong-Kyun and Park, Yongjun and Lee, Dongwon and Kim, Sang-Wook},
  booktitle={Proceedings of the 2021 IEEE International Conference on Data Mining (ICDM)},
  pages={290--299},
  year={2021},
  organization={IEEE}
}