This repo intends to be a tour through some recommendation algorithms in python using various dataset. Companion posts are:
At the moment the datasets included are:
the Ponpare coupon dataset, which corresponds to a coupon purchase prediction competition at Kaggle (i.e. recommending coupons to customers).
the Amazon Reviews dataset, in particular the 5-core Movies and TV reviews
Each of the two datasets is used to illustrate a set of different techniques, although I explored a wider range of techniques with the Ponpare dataset and the corresponding notebooks are, in general, more detailed.
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.
This is what you will find in the notebooks: