RecoTour

This repo intends to be a tour through some recommendation algorithms in python using various dataset. Companion posts are:

1. Recotour: a tour through recommendation algorithms in python

2. RecoTour II: neural recommendation algorithms

3. RecoTour III: Variational Autoencoders for Collaborative Filtering with Mxnet and Pytorch.

The repo is organised as follows:

1. recotour: this is the original "tour" through recommendation algorithms using the Ponpare coupon dataset. In particular, the algorithms included in the

   recotour

   directory are:
   1. Data processing, with a deep dive into feature engineering
   2. Most Popular recommendations (the baseline)
   3. Item-User similarity based recommendations
   4. kNN Collaborative Filtering recommendations
   5. GBM based recommendations using

      lightGBM

      with a tutorial on how to optimize gbms
   6. Non-Negative Matrix Factorization recommendations
   7. Factorization Machines (Steffen Rendle 2010) recommendations using

      xlearn

   8. Field Aware Factorization Machines (Yuchin Juan, et al, 2016) recommendations using

      xlearn

   9. Deep Learning based recommendations (Wide and Deep, Heng-Tze Cheng, et al, 2016) using

      pytorch

I have included a more modular (nicer looking) version of a possible final solution (described in

Chapter16_final_solution_Recommendations.ipynb

final_recommendations

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.py

py_scripts

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:

1. neural_cf: Neural Collaborative Filtering (Xiangnan He et al., 2017)
2. neuralgraphcf: Neural Graph Collaborative Filtering (Wang Xiang et al. 2019)
3. mult-vae: Variational Autoencoders for Collaborative Filtering (Dawen Liang et al,. 2018)

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.