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HMLET (Hybrid-Method-of-Linear-and-non-linEar-collaborative-filTering-method)

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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)

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