Paper Accepted to WSDM'22
Title: Linear, or Non-Linear, That is the Question!
docker pull pytorch/pytorch
docker run --gpus all -it --rm --privileged -v {local_path}:/HMLET pytorch/pytorch bash -c "pip install pandas && pip install scipy && pip install sklearn && pip install tensorboardX && pip install openpyxl && cd /HMLET && {train_model_command}"
python train.py --dataset {dataset_name} --model {model_variants}
Methods Proposal Background and Purpose
Which embedding propagation (linear & non-linear) is more appropriate to recommender systems?
HMLET (Hybrid-Method-of-Linear-and-non-linEar-collaborative-filTering-method)
Dynamically selecting the best propagation method for each node in a layer using gating networks.
Four variants of HMLET: HMLET (End), HMLET (Middle), HMLET (Front), HMLET (All)
Four variants of HMLET in terms of the location of the non-linear propagation.
HMLET (End) shows best performance among these variants
Focusing on gating in the third and fourth layers
The detailed workflow of HMLET (End)
