# ebnerd-benchmark

**Repository Path**: Ciward/ebnerd-benchmark

## Basic Information

- **Project Name**: ebnerd-benchmark
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-05-07
- **Last Updated**: 2024-05-07

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 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](https://docs.conda.io/projects/conda/en/latest/glossary.html#conda-environment) for environment management, and [VS Code](https://code.visualstudio.com/) 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 .
```

## Running GPU
```
tensorflow-gpu; sys_platform == 'linux'
tensorflow-macos; sys_platform == 'darwin'
```

# Data manipulation and enrichement
We have created a small [notebook](https://github.com/ebanalyse/ebnerd-benchmark/blob/main/examples/00_quick_start/dataset_ebnerd.ipynb) demo showing how one can join histories and create binary labels.

# Algorithms
To get started quickly, we have implemented a couple of News Recommender Systems, specifically, 
[Neural Recommendation with Long- and Short-term User Representations](https://aclanthology.org/P19-1033/) (LSTUR),
[Neural Recommendation with Personalized Attention](https://arxiv.org/abs/1907.05559) (NPA),
[Neural Recommendation with Attentive Multi-View Learning](https://arxiv.org/abs/1907.05576) (NAML), and
[Neural Recommendation with Multi-Head Self-Attention](https://aclanthology.org/D19-1671/) (NRMS). 
The source code originates from the brilliant RS repository, [recommenders](https://github.com/recommenders-team/recommenders). We have simply stripped it of all non-model-related code.

For now, we have created a [notebook](https://github.com/ebanalyse/ebnerd-benchmark/blob/main/examples/00_quick_start/nrms_ebnerd.ipynb) where we train NRMS on EB-NeRD - this is a very simple version using the demo dataset. More implementation examples will come at a later stage.