- Google Colab Notebook Link: Colab
- Annotated Tutorial link: Annotated LT-OCF
- Original Github repository: LT-OCF
This is the repository of our accepted CIKM 2021 paper "LT-OCF: Learnable-Time ODE-based Collaborative Filtering". Paper is available on arxiv
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}
}
If you use tutorial materials in your own studies, and work, please cite it by using the following:
@Misc{choi2021ltocf,
author = {Choi, Jeongwhan},
title = {{LT-OCF: Learnable-Time ODE-based Collaborative Filtering}},
howpublished = {\url{https://github.com/jeongwhanchoi/LT-OCF-Tutorial}},
month = November,
year = {since 2021}
}
conda env create -f environment.yml
conda activate lt-ocf
- 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
- 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