doublejone831/LT-OCF

LT-OCF: Learnable-Time ODE-based Collaborative Filtering, CIKM'21

LT-OCF: Learnable-Time ODE-based Collaborative Filtering

Introduction

This is the repository of our accepted CIKM 2021 paper "LT-OCF: Learnable-Time ODE-based Collaborative Filtering". Paper is available on arxiv

Citation

Please cite our paper if using this code.

@inproceedings{choi2021ltocf,
  title={LT-OCF: Learnable-Time ODE-based Collaborative Filtering},
  author={Choi, Jeongwhan and Jeon, Jinsung and Park, Noseong},
  booktitle={Proceedings of the 30th ACM International Conference on Information and Knowledge Management},
  year={2021},
  organization={ACM}
}

Our proposed LT-OCF

Our proposed dual co-evolving ODE

---

Setup Python environment for LT-OCF

Install python environment

conda env create -f environment.yml   

Activate environment

conda activate lt-ocf

---

Reproducibility

Usage

In terminal

• Run the shell file (at the root of the project)

# run lt-ocf (gowalla dataset, rk4 solver, learnable time)
sh ltocf_gowalla_rk4.sh

# run lt-ocf (gowalla dataset, rk4 solver, fixed time)
sh ltocf_gowalla_rk4_fixed.sh

Arguments (see more arguments in parse.py)

• gpuid
  • default: 0
• dataset
  • gowalla, yelp2018, amazon-book
• model
  • ltocf
• solver
  • euler, rk4, implicit_adams, dopri5
• adjoint
  • False, True
• K
  • 1, 2, 3, 4
• learnable_time
  • True, False
• dual_res
  • False, True