Source code of WWW 2022 Paper "Modeling User Behavior with Graph Convolution for Personalized Product Search".

• environment

conda env create -f SBG/air.yml

• prepare data

  • download amazon data from https://jmcauley.ucsd.edu/data/amazon/
  • specify the input and output dir in preprocessing/prepare_amazon.py
  • run preprocessing/prepare_amazon.py for corresponding datasets
  • config dataset in persearch.config.cfg_data

• reproduce the results

cd SBG
python main.py -d amazon_software@ -e 200 -r 5

• configuration

  • model configuration in persearch.config.cfg_model
  • training data generator configuration in persearch.config.cfg_gen
  • other command line interactions: persearch.args

• customize model

  • training data generator for the model
    • build Generator pipeline in persearch.gen
    • register gen in persearch.config.cfg_gen, add its name in your persearch.config.cfg_model with the key 'generator'
  • implement forward, do_train, and f_loss
  • doc refer to persearch.model.Base
    • example as persearch.model.zam.ZAM
  • register model in persearch.config.cfg_model
  • load it in model/__init__.py
  • write test in exps.py

• log

  • summary stored in logs/<dataset>/<dataset_ver>/<arg.caption>/<timestamp>