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Instructions for reproducing the experiments

Environmental Requirement

  1. You need to set up the environment for running the experiments (Anaconda 3-2020.02 or above and Python 3.7 or above). First, you can install Anaconda by following the instruction from the website.

    Next, you can create a virtual environment using the following commands:

    $ conda create -n SIGN python=3.7 anaconda
$ conda activate SIGN

  2. Install Pytorch with version 1.6.0 or later.

    For example (with CPU only version):

    $ pip install torch==1.6.0+cpu torchvision==0.7.0+cpu -f https://download.pytorch.org/whl/torch_stable.html

  3. Install torch-geometric package with version 1.4.3.

    (Note that it may need to appropriatly install the package torch-geometric based on the CUDA version (or CPU version if GPU is not avaliable). Please refer to the official website https://pytorch-geometric.readthedocs.io/en/1.4.3/notes/installation.html for more information of installing prerequisites of torch-geometric)

    For example (CPU only version):

    $ CUDA=cpu
$ TORCH=1.6.0
$ pip install torch-scatter==latest+${CUDA} -f https://pytorch-geometric.com/whl/torch-${TORCH}.html
$ pip install torch-sparse==latest+${CUDA} -f https://pytorch-geometric.com/whl/torch-${TORCH}.html
$ pip install torch-cluster==latest+${CUDA} -f https://pytorch-geometric.com/whl/torch-${TORCH}.html
$ pip install torch-spline-conv==latest+${CUDA} -f https://pytorch-geometric.com/whl/torch-${TORCH}.html
$ pip install torch-geometric==1.4.3

  4. Download the code and data.

    clone this repository:

    $ git clone https://github.com/ruizhang-ai/SIGN-Detecting-Beneficial-Feature-Interactions-for-Recommender-Systems.git

    and go to the folder of the repository:

    $ cd SIGN-Detecting-Beneficial-Feature-Interactions-for-Recommender-Systems/

    Now, our source codes are in the folder code/ and the datasets are in the folder data/.

  5. Install other packages listed in requirements.txt.

    $ pip install -r requirements.txt

Run the code

Go to the code/ folder and run the SIGN_main.py file:

$ cd code
$ python SIGN_main.py --dataset=frappe --pred_edges=1 --lr=0.05

Main arguments:

--dataset [frappe, ml-tag, twitter, DBPL_v1]: the dataset to run.
--dim: the embedding dimension of each attribute.
--hidden_layer: the MLP hidden layer for the inner interaction modeling function.
--l0_weight: weight of the L0 activation regularization term.
--l2_weight: weight of the L2 activation regularization term.
--lr: the learning rate.
--l0_para: l0 parameters, which are beta (temprature), zeta (interval_min) and gama (interval_max).
--pred_edges: 0 for using edges in dataset (may need *{dataset}.edge* file), 1 for predicting edges using L_0.

For more argument options, please refer to SIGN_main.py