This repository contains the core model we called "Collaborative filtering enhanced Content-based Filtering" published in our UMUAI article "Movie Genome: Alleviating New Item Cold Start in Movie Recommendation"
The article is accessible HERE, the dataset is accessble HERE.
Please cite our article if you use this repository or algorithm.
@Article{Deldjoo2019,
author="Deldjoo, Yashar
and Ferrari Dacrema, Maurizio
and Constantin, Mihai Gabriel
and Eghbal-zadeh, Hamid
and Cereda, Stefano
and Schedl, Markus
and Ionescu, Bogdan
and Cremonesi, Paolo",
title="Movie genome: alleviating new item cold start in movie recommendation",
journal="User Modeling and User-Adapted Interaction",
year="2019",
month="Feb",
day="26",
issn="1573-1391",
doi="10.1007/s11257-019-09221-y",
url="https://doi.org/10.1007/s11257-019-09221-y",
note="Source: \url{https://github.com/MaurizioFD/CFeCBF}",
}
See run_example.py for an example on how to use the code
Note that this repository requires Python 3.6
First we suggest you create an environment for this project using virtualenv (or another tool like conda)
First checkout this repository, then enter in the repository folder and run this commands to create and activate a new environment:
If you are using virtualenv:
virtualenv -p python3 CFeCBF
source CFeCBF/bin/activateIf you are using conda:
conda create -n CFeCBF python=3.6 anaconda
source activate CFeCBFThen install all the requirements and dependencies
pip install -r requirements.txtThis repository already contains compiled Cython code for Linux and Windows x86. In order to compile you must have installed: gcc and python3 dev, which can be installed with the following commands:
sudo apt install gcc
sudo apt-get install python3-devAt this point you can compile all Cython algorithms by running the following command. The script will compile within the current active environment. The code has been developed for Linux and Windows platforms. During the compilation you may see some warnings.
python run_compile_all_cython.py