TEARS

Text Representations for Scrutable Recommendations Accepted @ WebConf20225 (arXiv)

Data

We provide detailed instructions on how to reproduce the datasets used in the data directory

User Summaries

We provide user summaries in ./saved_user_summaries. These can be remade with

python -m preprocess.make_in_context_data --data_name=${data_name}

To simply use the preprocessed data directly first run:

tar xvzf data_preprocessed.tar.gz
tar xvzf saved_user_summary.tar.gz

tar xvzf data.tar.gz contains all original data, which can be redownloaded using the instructions provided in data, but we include here for convinience.

tar xvzf data_preprocessed.tar.gz contains all relevant processed data that is needed to run the code.

saved_user_summary.tar.gz contains all LLaMA and GPT user summaries, as well as augmented summaries used to evaluate large-scope and fine-grained controllability.

Execution

We provide an example execution command below, but note other examples given in /execute

We implement EASE,MVAE,MDAE, MacridVAE,RecVAE and their appropriate TEARS models

To execute the training pipeline run:

python -m trainer.train \
    --seed=2024 \
    --embedding_module=${module} \
    --data_name=${dataset} \
    --dropout=.4 \
    --epochs=30 \
    --lora_alpha=16 \
    --lora_r=64 \
    --bs=64 \
    --lr=.0001 \
    --scheduler=None \
    --epsilon=.5 \
    --eval_control \
    --wandb

Requirements

Acknowledgements

We thank the authors of the following repositories for their useful codebases that where a key role in the execution of this project:

MVAE/MDAE https://github.com/younggyoseo/vae-cf-pytorch

RecVAE https://github.com/ilya-shenbin/RecVAE

MacridVAE (adapted to torch from) https://jianxinma.github.io/disentangle-recsys.html