Conditional Ranking-Critical Training for Collaborative Filtering

This repository implements Ranking-Critical Training (RaCT) for Collaborative Filtering with additional information to improve quality of recommendation

This code is originally created from https://github.com/samlobel/RaCT_CF/ and modified to be able to use side information

The main result of this paper can be reproduced via running the scripts/main_vae.ipynb file:

Running the code

1. Install the requirements in a python3 environment

   mkvirtualenv --python=python3 CRITIC_CF && pip install -r requirements.txt

2. Install Tensorflow, version range >=1.10.0,<=1.14.

3. Download the dataset you want to run with

   python setup_data.py --dataset=DATASET_OF_CHOICE

Default is ml-100k, which is the smallest dataset. Other options are ml-1m, ml-20m, netflix-prize and msd. Or, run with dataset=all to download all.

4. setup side information

   python setup_side_data.py

This script is only applied to ml-100k dataset.

5. To train a model, run one of the scripts in the scripts directory named main_*.ipynb (see the Main Scripts section of the Readme). These scripts were used to generate the models and data in this paper. Or, create your own by modifying the hyperparameters or models used in one of these scripts.

6. The train method saves a model in a nested directory determined by its hyperparameters, within the checkpoints directory. It stores the data necessary for plotting in the plotting_data directory, and logs to the logging directory.

7. Passing these same hyperparameters to the test method as you did to train method will run the trained model against the held-out test data. After running test, results are written to the TEST_RESULTS.txt file, as well as to the console.

Utils

The importable code used in the training scripts is located in the ./utils directory.

• warp_utils.py contain the logic for implementing a listwise WARP-loss
• lambdarank_utils.py contains the logic for implementing LambdaRank
• evaluation_functions.py contains the logic for the ranking objective functions
• base_models.py and models.py contain the different models we train.
• training.py contains the train and test function used to run experiments.
• data_loaders.py implements importing the downloaded files as Tensorflow Datasets.

Main Scripts

All of the scripts used to generate data are located in the ./scripts directory.

• main_vae.ipynb: This provides the paper's main result, by running the MultiVAE model with and without the critic, on all datasets.
• main_vae_with_userinfo.py is used to generate results for MultiVAE with user-category side information.
• main_vae_with_userinfo2.py is used to generate results for MultiVAE with user-item side information.