"Learning Recommenders for Implicit Feedback with Importance Resampling" (WWW2022) The codes are tested in Pytorch
- data
- gowalla, yelp, amazoni
- d
- embedding size
- m, model
- 0: matrix factorization
- 1: NCF
- 2: GMF
- 3: MLP
- sampler
- 0: uniform
- 2: AdaSIR uniform
- 3: popularity
- 5: AdaSIR pop
- 7: AdaSIR uniform + rank estimation
- 8: DNS
- 9: Adaptive kernel(only works for matrix factorization)
python main_more.py --sampler 0 --weighted
for PRIS(U)
python main_more.py --sampler 2 --weighted
for AdaSIR
or
sh run.sh