CoRML

PyTorch version (Default) | CuPy version

This is the official implementation of our The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2023) paper:

Tianjun Wei, Jianghong Ma, Tommy W.S. Chow. Collaborative Residual Metric Learning. [arXiv] [Paper]

Requirements

The model implementation ensures compatibility with the Recommendation Toolbox RecBole (Github: Recbole).

The requirements of the running environement:

• Python: 3.8+
• PyTorch: 1.9.0+
• RecBole: 1.2.0

Dataset

Here we only put zip files of datasets in the respository due to the storage limits. To use the dataset, run

unzip -o "Data/*.zip"

to unzip the dataset files.

If you like to test CoRML on the custom dataset, please place the dataset files in the following path:

.
|-Data
| |-[CUSTOM_DATASET_NAME]
| | |-[CUSTOM_DATASET_NAME].user
| | |-[CUSTOM_DATASET_NAME].item
| | |-[CUSTOM_DATASET_NAME].inter

And create [CUSTOM_DATASET_NAME].yaml in ./Params with the following content:

dataset: [CUSTOM_DATASET_NAME]

For the format of each dataset file, please refer to RecBole API.

Hyperparameter

For each dataset, the optimal hyperparameters are stored in Params/[DATASET].yaml. To tune the hyperparamters, modify the corresponding values in the file for each dataset.

The main hyparameters of CoRML are listed as follows:

• lambda ($\lambda$): Weights for $\mathbf{H}$ and $\mathbf{G}$ in preference scores
• dual_step_length ($\rho$): Dual step length of ADMM
• l2_regularization ($\theta$): L2-regularization for learning weight matrix $\mathbf{H}$
• item_degree_norm ($t$): Item degree norm for learning weight matrix $\mathbf{H}$
• global_scaling ($\epsilon$): Global scaling in approximated ranking weights (in logarithm scale)
• user_scaling ($t_u$): User degree scaling in approximated ranking weights

Running on small partitions

For datasets containing a large number of items, calculating and storing the complete item-item matrix may lead to out-of-memory problem in environments with small GPU memory. Therefore, we have added the code for graph spectral partitioning to learn the item-item weight matrix on each small partitioned item set. The code was modified based on our previous work FPSR.

Hyperparameter partition_ratio is used to control the maximum ratio of the partitioned set of items relative to the complete set, ranging from 0 to 1. When partition_ratio is set to 1, no partitioning will be performed.

Sparse approximation

To maintain consistency, we perform a sparse approximation of the derived matrice $\mathbf{H}$ in CoRML by setting the entries to 0 where $\lvert \mathbf{H} \rvert \leq \eta$. The threshold $\eta$ will be adjusted so that the storage size of the sparse matrix $\mathbf{H}_{Sparse}$ is less than other types of models with embedding size 64.

Hyperparameter sparse_approx is used to control the sparse approximation. When sparse_approx is set to False, no sparse approximation will be performed.

Running

The script run.py is used to reproduced the results presented in paper. Train and evaluate CoRML on a specific dataset, run

python run.py --dataset DATASET_NAME

Google Colab

We also provide Colab notebook version of CoRML, you can click here to open Google Colab, select the runtime type as GPU, and run the model.

Citation

If you wish, please cite the following paper:

@inproceedings{CoRML,
  author = {{Wei}, Tianjun and {Ma}, Jianghong and {Chow}, Tommy W.~S.},
  title = {Collaborative Residual Metric Learning},
  year = {2023},
  isbn = {9781450394086},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3539618.3591649},
  doi = {10.1145/3539618.3591649},
  booktitle = {Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval},
  pages = {1107–1116},
  numpages = {10},
  location = {Taipei, Taiwan},
  series = {SIGIR '23}
}

@InProceedings{FPSR,
  author    = {{Wei}, Tianjun and {Ma}, Jianghong and {Chow}, Tommy W.~S.},
  booktitle = {Proceedings of the ACM Web Conference 2023},
  title     = {Fine-tuning Partition-aware Item Similarities for Efficient and Scalable Recommendation},
  year      = {2023},
  address   = {New York, NY, USA},
  publisher = {Association for Computing Machinery},
  series    = {WWW '23},
  doi       = {10.1145/3543507.3583240},
  location  = {Austin, TX, USA},
  numpages  = {11},
  url       = {https://doi.org/10.1145/3543507.3583240},
}

License

This project is licensed under the terms of the MIT license.