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-# 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)
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