Simplifying Content-Based News Recommendation

A NEW VERSION OF THE FRAMEWORK IS OUT IN NewsRecLib

Description

This is the code accompanying the paper Simplifying Content-Based Neural News Recommendation: On User Modeling and Training Objectives in which we propose a unified framework allowing for a systematic and fair comparison of news recommenders across three crucial design dimensions: (i) candidate-awareness in user modeling, (ii) click behavior fusion, and (iii) training objectives.

Project Structure

The directory structure of the project looks like this:

├── configs                   <- Hydra configuration files
│   ├── callbacks                <- Callbacks configs
│   ├── datamodule               <- Datamodule configs
│   ├── debug                    <- Debugging configs
│   ├── experiment               <- Experiment configs
│   ├── extras                   <- Extra utilities configs
│   ├── hparams_search           <- Hyperparameter search configs
│   ├── hydra                    <- Hydra configs
│   ├── local                    <- Local configs
│   ├── logger                   <- Logger configs
│   ├── model                    <- Model configs
│   ├── paths                    <- Project paths configs
│   ├── trainer                  <- Trainer configs
│   │
│   ├── eval.yaml             <- Main config for evaluation
│   └── train.yaml            <- Main config for training
│
├── data                   <- Project data
│
├── logs                   <- Logs generated by hydra and lightning loggers
│
├── scripts                <- Shell scripts
│
├── src                    <- Source code
│   ├── datamodules              <- Lightning datamodules
│   ├── models                   <- Lightning models
│   ├── utils                    <- Utility scripts
│   │
│   ├── eval.py                  <- Run evaluation
│   └── train.py                 <- Run training
│
├── .gitignore                <- List of files ignored by git
├── requirements.txt          <- File for installing python dependencies
├── setup.py                  <- File for installing project as a package
└── README.md

Data

We use the MIND dataset in all experiments.
The datasets are automatially downloaded, cached, and pre-processed when running the train.py pipeline.
Alternatively, the datasets can be manually downloaded into the data directory using the URLs from the MIND data config.

How to run

Install dependencies

# clone project
git clone [email protected]:andreeaiana/simplifying_nnr.git
cd simplifying_nnr

# [OPTIONAL] create conda environment
conda create -n simplifying_nnr_env python=3.9
conda activate simplifying_nnr_env

# install pytorch according to instructions
# https://pytorch.org/get-started/

# install requirements
pip install -r requirements.txt

Train model with chosen experiment configuration from configs/experiment/

# train on CPU
python src/train.py experiment=experiment_name.yaml trainer=cpu

# train on GPU
python src/train.py experiment=experiment_name.yaml trainer=gpu

You can override any parameter from command line like this

python src/train.py trainer.max_epochs=20 datamodule.batch_size=64

Citation

@article{iana2023simplifying,
    title={Simplifying Content-Based Neural News Recommendation: On User Modeling and Training Objectives},
    author={Andreea Iana and Goran Glavaš and Heiko Paulheim},
    booktitle={Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval},
    pages={2384--2388},
    year={2023}
}