Paper Accepted to WSDM'22

• Title: Linear, or Non-Linear, That is the Question!

How to Use

• Docker environments

docker pull pytorch/pytorch

• Docker run

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}"

• Train model

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?

Methods

• 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)

  • HMLET (End) shows best performance among these variants

  • Focusing on gating in the third and fourth layers

  • The detailed workflow of HMLET (End)