jin530 / SWalk

This is the official code for the WSDM 2022 paper: 'S-Walk: Accurate and Scalable Session-based Recommendation with Random Walks'.

SWalk

This is the official code for the WSDM 2022 paper:
[S-Walk: Accurate and Scalable Session-based Recommendation with Random Walks.] (https://arxiv.org/abs/2201.01091).

We implemented our model based on the session-recommedndation framework session-rec, and you can find the other session-based models and detailed usage on there.
Thanks for sharing the code.

README.md and the comments in source code will be updated, again.

The slides can be found here.

Dataset

Datasets can be downloaded from:
https://drive.google.com/drive/folders/1ritDnO_Zc6DFEU6UND9C8VCisT0ETVp5

• Unzip any dataset file to the data folder, i.e., rsc15-clicks.dat will then be in the folder data/rsc15/raw
• Run a configuration with the following command: For example: python run_preprocesing.py conf/preprocess/window/rsc15.yml

Basic Usage

• Change the expeimental settings and the model hyperparameters using a configuration file *.yml.
  
• When a configuration file in conf/in has been executed, it will be moved to the folder conf/out.
• Run run_config.py with configuaration folder arguments to train and test models.
  For example: python run_confg.py conf/in conf/out

Running SLIST

• The yml files for slist used in paper can be found in conf/save_swalk

Requirements

• Python 3
• NumPy
• Pyyaml
• SciPy
• Sklearn
• Pandas
• Psutil