Repository for the course in Recommended Systems at University of Amsterdam 2024
We recommend conda for environment management, and VS Code for development. To install the necessary packages and run the example notebook:
# 1. Clone this repo using command line:
git clone https://github.com/danilotpnta/MTRec-RecSys.git
cd MTRec-RecSys/
Make sure you deactivate any other environemnt i.e. anaconda. For compatibility, we recommend to use python 3.11
Now, let's create a virtual environment (venv) and install the dependencies specified in the pyproject.toml file.
# 2. Create venv environment:
python3 -m venv venv
source venv/bin/activate
# 3. Install the core ebrec package to the enviroment:
pip install .
tensorflow-gpu; sys_platform == 'linux'
tensorflow-macos; sys_platform == 'darwin'
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.
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.
We have created a notebook where we train NRMS on EB-NeRD - this is a very simple version using the demo dataset.
In the dataset_ebnerd demo, we show how one can join histories and create binary labels.