Welcome to the online appendix of my thesis. This appendix goes through the code of my thesis and explains the gini calculation. The WMF notebook explains the process of RS evaluation, which is copied in the LMF and kNN notebooks. Unfortunately, the LMF notebook is a bit heavy in its computations, which let to a memory error on my laptop. Copying the code directly into a python-file and running it on PyCharm fixed the problem for me. So let's start with recommending after these short notes.

First of, some programs are necessary for the python code, namely: Anaconda, Implicit.py and Visual Tools.

Anaconda can be downloaded on https://www.anaconda.com/download/ (download the python 3.7 version).

Visual Tools can be downloaded on https://www.visualstudio.com/downloads/#build-tools-for-visual-studio-2017 (it's one of the two free downloads, but I don't know which).

Implicit.py needs to be installed through the Anaconda prompt, and here is a YouTube video that explains that process: https://www.youtube.com/watch?v=Z_Kxg-EYvxM (the necessary command is "pip install implicit.py").

Second, the dataset is available on request, by mailing p.j.manders@uvt.nl. Same goes for the actual code of the RS model, which is a little bit harder to understand.

Furthermore, if anything is not clear or some code is not working, feel free to contact me on my email.

Happy coding!