Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/amitkaps/recommendation

Recommendation System using ML and DL
https://github.com/amitkaps/recommendation

colloborative-filtering deep-learning hybrid-recommender primer recsys workshop

Last synced: 7 days ago
JSON representation

Recommendation System using ML and DL

Host: GitHub
URL: https://github.com/amitkaps/recommendation
Owner: amitkaps
License: mit
Created: 2016-11-14T13:00:55.000Z (almost 8 years ago)
Default Branch: master
Last Pushed: 2022-12-08T06:10:37.000Z (almost 2 years ago)
Last Synced: 2024-04-15T00:36:01.593Z (7 months ago)
Topics: colloborative-filtering, deep-learning, hybrid-recommender, primer, recsys, workshop
Language: Jupyter Notebook
Homepage:
Size: 60.2 MB
Stars: 417
Watchers: 16
Forks: 152
Open Issues: 21
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

        # Recommendation Systems

This is a workshop on using Machine Learning and Deep Learning Techniques to build Recommendation Systesm

- **Theory**: ML & DL Formulation, Prediction vs. Ranking, Similiarity, Biased vs. Unbiased

- **Paradigms**: Content-based, Collaborative filtering, Knowledge-based, Hybrid and Ensembles

- **Data**: Tabular, Images, Text (Sequences)

- **Models**: (Deep) Matrix Factorisation, Auto-Encoders, Wide & Deep, Rank-Learning, Sequence Modelling

- **Methods**: Explicit vs. implicit feedback, User-Item matrix, Embeddings, Convolution, Recurrent, Domain Signals: location, time, context, social,

- **Process**: Setup, Encode & Embed, Design, Train & Select, Serve & Scale, Measure, Test & Improve

- **Tools**: python-data-stack: numpy, pandas, scikit-learn, keras, spacy, implicit, lightfm

## Notes & Slides

- Basics: [Deep Learning](Notes/Deep-Learning-Basics.pdf)

- AI Conference 2019: [WhiteBoard Notes](Notes/AIConf-CA-2019-Notes.pdf) | [In-Class Notebooks](https://notes.pipal.in/2019/AIConf-CA/) 

## Notebooks

- [Movies - Movielens](MovieLens)

    - [01-Acquire](MovieLens/01-Acquire.ipynb)

    - [02-Augment](MovieLens/02-Augment.ipynb)

    - [03-Refine](MovieLens/03-Refine.ipynb)

    - [04-Transform](MovieLens/04-Evaluation.ipynb)

    - [05-Evaluation](MovieLens/05-Evaluation.ipynb)

    - [06-Model-Baseline](Movielens/06-Model-Baseline.ipynb)

    - [07-Feature-extractor](Movielens/07-Feature-Extractor.ipynb)

    - [08-Model-Matrix-Factorization](Movielens/08-Model-MF-Linear.ipynb)

    - [09-Model-Matrix-Factorization-with-Bias](Movielens/09-MF-Linear-Bias.ipynb)

    - [10-Model-MF-NNMF](Movielens/10-Model-MF-NNMF.ipynb)

    - [11-Model-Deep-Matrix-Factorization](Movielens/11-Model-Deep-Factorisation.ipynb)

    - [12-Model-Neural-Collaborative-Filtering](Movielens/12-Neural-CF.ipynb)

    - [13-Model-Implicit-Matrix-Factorization](Movielens/13-Implicit-CF.ipynb)

    - [14-Features-Image](Movielens/14-Image-Features.ipynb)

    - [15-Features-NLP](Movielens/15-Doc-Embedding.ipynb)

- [Ecommerce - YooChoose](YooChoose)

    - [01-Data-Preparation](YooChoose/01-Data-Preparation.ipynb)     

    - [02-Models](YooChoose/02-Models.ipynb)

    

- [News - Hackernews](HackerNews)

- [Product - Groceries](Groceries)

    

## Python Libraries

Deep Recommender Libraries

- [Tensorrec](https://github.com/jfkirk/tensorrec) - Built on Tensorflow

- [Spotlight](https://github.com/maciejkula/spotlight) - Built on PyTorch

- [TFranking](https://github.com/tensorflow/ranking) - Built on TensorFlow (Learning to Rank)

Matrix Factorisation Based Libraries

- [Implicit](https://github.com/benfred/implicit) - Implicit Matrix Factorisation

- [QMF](https://github.com/quora/qmf) - Implicit Matrix Factorisation

- [Lightfm](https://github.com/lyst/lightfm) - For Hybrid Recommedations

- [Surprise](http://surpriselib.com/) - Scikit-learn type api for traditional alogrithms

Similarity Search Libraries

- [Annoy](https://github.com/spotify/annoy) - Approximate Nearest Neighbour

- [NMSLib](https://github.com/nmslib/nmslib) - kNN methods

- [FAISS](https://github.com/facebookresearch/faiss) - Similarity search and clustering

Content-based Libraries

- [Cornac](https://github.com/PreferredAI/cornac) - Leverage Auxiliary Data (Images, Text, Social Networks)

## Learning Resources

### Reference Slides

- [Deep Learning in RecSys by Balázs Hidasi](http://pro.unibz.it/projects/schoolrecsys17/DeepLearning.pdf)

- [Lessons from Industry RecSys by Xavier Amatriain](http://pro.unibz.it/projects/schoolrecsys17/RecsysSummerSchool-XavierAmatriain.pdf)

- [Architecting Recommendation Systems by James Kirk](https://www.slideshare.net/JamesKirk58/boston-ml-architecting-recommender-systems)

- [Recommendation Systems Overview by Raimon and Basilico](http://nn4ir.com/ecir2018/slides/08_RecommenderSystems.pdf)

### Benchmarks

- [MovieLens Benchmarks for Traditional Setup](https://github.com/microsoft/recommenders/blob/master/benchmarks/movielens.ipynb)

- [Microsoft Tutorial on Recommendation System at KDD 2019](https://github.com/microsoft/recommenders)

### Algorithms & Approaches

- [Collaborative Filtering for Implicit Feedback Datasets](http://yifanhu.net/PUB/cf.pdf)

- [Bayesian Personalised Ranking for Implicit Data](https://arxiv.org/pdf/1205.2618)

- [Logistic Matrix Factorisation](https://web.stanford.edu/~rezab/nips2014workshop/submits/logmat.pdf)

- [Neural Network Matrix Factorisation](https://arxiv.org/abs/1511.06443)

- [Neural Collaborative Filtering](https://arxiv.org/abs/1708.05031)

- [Variational Autoencoders for Collaborative Filtering](https://arxiv.org/abs/1802.05814)

### Evaluations

- [Evaluating Recommendation Systems](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/EvaluationMetrics.TR_.pdf)