CausalDiffRec

• Model framework

Requirements

• torch==2.1.1+cu121
• torch_geometric==2.5.3
• torchaudio==2.1.1+cu121
• torchvision==0.16.1+cu121
• tornado==6.4.1
• dgl==2.0.0+cu121

Run and reproduce

Run following python code (available dataset: "Yelp2018", "Douban") with default hyperparameters to reproduce our results.

python train.py --dataset yelp2018 

python train.py --dataset douban 

Dataset

| Dataset | #Users | #Items | #Interactions | Density | |:----------:|:--------:|:--------:|:---------------:|:-----------:| | Food | 7,809 | 6,309 | 216,407 | 4.4 × 10⁻³ | | KuaiRec | 7,175 | 10,611 | 1,153,797 | 1.5 × 10⁻³ | | Yelp2018 | 8,090 | 13,878 | 398,216 | 3.5 × 10⁻³ | | Douban | 8,735 | 13,143 | 354,933 | 3.1 × 10⁻³ |

We retain only those users with at least 15 interactions on the Food dataset, at least 25 interactions on the Yelp2018 and Douban datasets, and items with at least 50 interactions on these datasets. For all three datasets, only interactions with ratings of 4 or higher are considered positive samples. For the KuaiRec dataset, interactions with a watch ratio of 2 or higher are considered positive samples.

Acknowledgements

We are particularly grateful to the authors of DiffRec, Graphood-EERM, and SELFRec as parts of our code implementation were derived from their work. We have cited the relevant references in our paper.

Reference