InDuctive Collaborative Filtering (IDCF)

The codes and data used in ICML'21 paper Towards Open-World Recommendation: An Inductive Model-Based Collaborative Filtering Apparoach. There is a Chinese tutorial Blog that introduces this work in an easy-to-follow manner.

This work proposes a new collaborative filtering approach for recsys. The new method could achieve inductive learning for new users in testing set and also help to address cold-start problem on user side.

Dependency

Python 3.8, Pytorch 1.7, Pytorch Geometric 1.6

Download trained model and data

The trained model and preprocessed data can be downloaded by the Google drive

https://drive.google.com/drive/folders/1rTfOKZJ-zYrNY9hDUtU9H-UG9fPPQOds?usp=sharing

You can make a directory ./data in the root and download the data into it.

Reproduce results

To reproduce the results in our paper (i.e. Table 2, 3, 4), you need to first download the trained model and data to corresponding folders and run the test.py script in each folder. Take Movielens-1M dataset as an example. You need to first download the folder data/ml-1m.pkl from the Google drive to ./data in your own computer and download model/ml-1m/ to ./code/ml-1m/ in your computer. Then you can run

python ./code/ml-1m/IDCF-NN/test-1m.py

to reproduce the results of IDCF-NN model on few-shot query users on Movielens-1M. Also, you can run

python ./code/ml-1m/IDCF-NN/test-1m.py --extra

to reproduce the results of IDCF-NN model on zero-shot new users on Movielens-1M.

Run the code for training

Our model needs a two-stage training. To train the model from the beginning, you can run two scripts in order.

First, you need to run

python ./code/ml-1m/IDCF-NN/pretrain-1m.py

to pretrain the matrix factorization model. Alternatively, you can skip the pretrain stage by directly using our pretrained model, i.e., download the model file from the path model/ml-1m/IDCF-NN/pretrain-1m/model.pkl in the Google drive to ./code/ml-1m/IDCF-NN/pretrain-1m/model.pkl in your computer.

Second, you need to run the script train-1m.py.

python ./code/ml-1m/IDCF-NN/train-1m.py

Also, in our paper, we consider two scenarios: inductive learning for interpolation (for few-shot query users) and inductive learning for extrapolation (for new test users). For running the former case, you need to set the variable EXTRA as False in train-1m.py. For the latter case, you can set EXTRA=True in train-1m.py.

For model details, please refer to our paper. If you have any question, feel free to contact via email.

If you found the codes or datasets useful, please consider cite our paper:

    @inproceedings{wu2021idcf,
    title = {Towards Open-World Recommendation: An Inductive Model-Based Collaborative Filtering Apparoach},
    author = {Qitian Wu and Hengrui Zhang and Xiaofeng Gao and Junchi Yan and Hongyuan Zha},
    booktitle = {International Conference on Machine Learning (ICML)},
    year = {2021}
    }