AHNS

PyTorch Implementation for Adaptive Hardness Negative Sampling for Collaborative Filtering, AAAI2024

--- Based on "MixGCF: An Improved Training Method for Graph Neural Network-based Recommender Systems", https://github.com/huangtinglin/MixGCF

Environment Requirement

The code has been tested running under Python 3.7.6. The required packages are as follows:

• pytorch == 1.7.0
• numpy == 1.20.2
• scipy == 1.6.3
• sklearn == 0.24.1
• prettytable == 2.1.0

Training

The training commands are as following:

python main.py --dataset ml --gnn lightgcn --dim 64 --lr 0.001 --batch_size 2048 --gpu_id 0 --context_hops 0 --ns ahns --alpha 0.1 --beta 0.4 --n_negs 16

python main.py --dataset phone --gnn lightgcn --dim 64 --lr 0.001 --batch_size 2048 --gpu_id 0 --context_hops 0 --ns ahns --alpha 1.0 --beta 0.1 --n_negs 32

python main.py --dataset sport --gnn lightgcn --dim 64 --lr 0.001 --batch_size 2048 --gpu_id 0 --context_hops 0 --ns ahns --alpha 0.5 --beta 0.1 --n_negs 32

python main.py --dataset tool --gnn lightgcn --dim 64 --lr 0.001 --batch_size 2048 --gpu_id 0 --context_hops 0 --ns ahns --alpha 1.0 --beta 0.1 --n_negs 32

The ipynb files are also provided.

Datasets

We use four processed datasets: MovieLens-1M, Amazon-Phones, Amazon-Sports and Amazon-Tools. The processing code is also provided.