Inverse Learning with Extremely Sparse Feedback for Recommendation.

This is the pytorch implementation of our paper.

Environment

• Anaconda 3
• python 3.7.3
• pytorch 1.4.0
• numpy 1.16.4

Usage

Training

python main.py --dataset=$1 --model=$2 --drop_rate=$3 --num_gradual=$4 --gpu=$5

The output will be in the ./log/xxx folder.

Inference

We provide the code to inference based on the well-trained model parameters.

python inference.py --dataset=$1 --model=$2 --drop_rate=$3 --num_gradual=$4 --gpu=$5

Examples

1. Train GMF on ml1m:

python main.py --dataset=ml1m --model=GMF --drop_rate=0.1 --num_gradual=30000 --gpu=0

We release all training logs in ./log folder. The hyperparameter settings can be found in the log file.

Acknowledgment

Thanks to the DenoisingRec implementation:

• DenoisingRec.