A tour through recommendation algorithms in python [IN PROGRESS]
This repo intends to be a tour through some recommendation algorithms in python using various dataset. Companion posts are:
The repo is organised as follows:
lightGBMwith a tutorial on how to optimize gbms
I have included a more modular (nicer looking) version of a possible final solution (described in
Chapter16_final_solution_Recommendations.ipynb) in the directory
In addition, I have included an illustration of how to use other evaluation metrics apart from the one shown in the notebooks ( the mean average precision or MAP) such as the Normalized Discounted Cumulative Gain (NDCG). This can be found in
using_ncdg.pyin the directory
In addition, there are other, DL-based recommendation algorithms that use mainly the Amazon Reviews dataset, in particular the 5-core Movies and TV reviews. These are:
The core of the repo are the notebooks in each directory. They intend to be self-contained and in consequence, there is some of code repetition. The code is, of course, "notebook-oriented". The notebooks have plenty of explanations and references to relevant papers or packages. My intention was to focus on the code, but you will also find some math.
I hope the code here is useful to someone. If you have any idea on how to improve the content of the repo, or you want to contribute, let me know.