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Contributors

Introduction

Hello there 👋🏽

We recommend to check the repository frequently, as we are updating and documenting it along the way!

EBNeRD

Ekstra Bladet Recommender System repository, created for the RecSys'24 Challenge.

Getting Started

We recommend conda for environment management, and VS Code for development. To install the necessart packages and run the example notebook:

# 1. Create and activate a new conda environment
conda create -n <environment_name> python=3.11
conda activate <environment_name>

# 2. Clone this repo within VSCode or using command line:
git clone https://github.com/ebanalyse/ebnerd-benchmark.git

# 3. Install the core ebrec package to the enviroment:
pip install .

We have experienced issues installing tensorflow for M1 Macbooks (sys_platform == 'darwin') when using conda. To avoid this, we suggest to use venv if running on macbooks.

python3 -m .venv .venv
source  .venv/bin/activate

Installing .venv in project folder:

conda create -p .venv python==3.11.8
conda activate ./.venv

Running GPU

tensorflow-gpu; sys_platform == 'linux'
tensorflow-macos; sys_platform == 'darwin'

Algorithms

To get started quickly, we have implemented a couple of News Recommender Systems, specifically, Neural Recommendation with Long- and Short-term User Representations (LSTUR), Neural Recommendation with Personalized Attention (NPA), Neural Recommendation with Attentive Multi-View Learning (NAML), and Neural Recommendation with Multi-Head Self-Attention (NRMS). The source code originates from the brilliant RS repository, recommenders. We have simply stripped it of all non-model-related code.

Notebooks

To help you get started, we have created a few notebooks. These are somewhat simple and designed to get you started. We do plan to have more at a later stage, such as reproducible model trainings. The notebooks were made on macOS, and you might need to perform small modifications to have them running on your system.

Model training

We have created a notebook where we train NRMS on EB-NeRD - this is a very simple version using the demo dataset.

Data manipulation and enrichment

In the dataset_ebnerd demo, we show how one can join histories and create binary labels.

Reproduce EB-NeRD Experiments

Activate your enviroment:

conda activate <environment_name>

NRMSModel

python examples/reproducibility_scripts/ebnerd_nrms.py
  --datasplit ebnerd_small \
  --epochs 5 \
  --bs_train 32 \
  --bs_test 32 \
  --history_size 20 \
  --npratio 4 \
  --transformer_model_name FacebookAI/xlm-roberta-large \
  --max_title_length 30 \
  --head_num 20 \
  --head_dim 20 \
  --attention_hidden_dim 200 \
  --learning_rate 1e-4 \
  --dropout 0.20

Tensorboards:

tensorboard --logdir=ebnerd_predictions/runs

NRMSDocVec

python examples/reproducibility_scripts/ebnerd_nrms_docvec.py \
  --datasplit ebnerd_small \
  --epochs 5 \
  --bs_train 32 \
  --history_size 20 \
  --npratio 4 \
  --document_embeddings Ekstra_Bladet_contrastive_vector/contrastive_vector.parquet \
  --head_num 16 \
  --head_dim 16 \
  --attention_hidden_dim 200 \
  --newsencoder_units_per_layer 512 512 512 \
  --learning_rate 1e-4 \
  --dropout 0.2 \
  --newsencoder_l2_regularization 1e-4

Tensorboards:

tensorboard --logdir=ebnerd_predictions/runs