https://github.com/jrzaurin/recotour

A tour through recommendation algorithms in python [IN PROGRESS]
https://github.com/jrzaurin/recotour

collaborative-filtering deep-learning lightgbm matrix-factorization python3 recommendation-algorithms

Last synced: 2 months ago
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A tour through recommendation algorithms in python [IN PROGRESS]

• Host: GitHub
• URL: https://github.com/jrzaurin/recotour
• Owner: jrzaurin
• Created: 2018-05-22T19:06:23.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2024-12-26T18:38:27.000Z (6 months ago)
• Last Synced: 2025-01-27T15:27:30.169Z (5 months ago)
• Topics: collaborative-filtering, deep-learning, lightgbm, matrix-factorization, python3, recommendation-algorithms
• Language: Jupyter Notebook
• Homepage:
• Size: 6.56 MB
• Stars: 176
• Watchers: 21
• Forks: 38
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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# 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](https://medium.com/datadriveninvestor/recotour-a-tour-through-recommendation-algorithms-in-python-52d780628ab9)

2. [RecoTour II: neural recommendation algorithms](https://towardsdatascience.com/recotour-ii-neural-recommendation-algorithms-49733938d56e)

3. [RecoTour III: Variational Autoencoders for Collaborative Filtering with Mxnet and Pytorch](https://jrzaurin.github.io/infinitoml/2020/05/15/mult-vae.html).

The repo is organised as follows:

1. **recotour**: this is the original "tour" through recommendation algorithms

using the [Ponpare](https://www.kaggle.com/c/coupon-purchase-prediction)

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`) in

  the directory `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](https://en.wikipedia.org/wiki/Discounted_cumulative_gain)). This can

  be found in `using_ncdg.py` in the directory `py_scripts`.

In addition, there are other, DL-based recommendation algorithms that use

mainly the [Amazon Reviews](http://jmcauley.ucsd.edu/data/amazon/) dataset,

in particular the 5-core Movies and TV reviews. These are:

2. **neural_cf**: Neural Collaborative Filtering (Xiangnan He et al., 2017)

3. **neural_graph_cf**: Neural Graph Collaborative Filtering (Wang Xiang et al. 2019)

4. **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.