Personalized Machine Learning Code Workbooks

This repo contains official code workbooks for textbook Personalized Machine Learning, about following topics:

Chapter	Topics	Implemented (or Used) Models
2	Regression and Feature Engineering	Linear Regression (sklearn)
3	Classification and the Learning Pipeline	Linear Regression (TF, PyTorch), Logistic Regression (TF, PyTorch), Ridge Model (sklearn)
4	Introduction to Recommender Systems	Similarity-based Recommendation Models
5	Model-based Approaches to Recommendation	Latent Factor Model (surpirse, TF, PyTorch), BPR (implicit, TF, PyTorch)
6	Content and Structure in Recommender Systems	Factorization Machine (pyFM), BPR (TF, PyTorch)
7	Temporal and Sequential Models	AutoRegression (sklearn), MF (TF, PyTorch), FMC(TF, PyTorch), FPMC (TF, PyTorch)
8	Personalized Models of Text	BoW, N-Gram, TF-IDF, Ridge Model (sklearn), word2vec (gensim), item2vec (gensim),
9	Personalized Models of Visual Data	Visual Compatibility Model (TF, PyTorch),
10	The Consequences of Personalized Machine Learning	Fairness-Aware Latent Factor Model (TF, PyTorch), BPR (implicit)

For each chapter, we include a requirements.txt file and a jupyter notebook Chapter_*.ipynb. To run the code in notebook of chapter i, you need:

1. open folder Chapter_i;
2. use pip install -r requirements.txt to install the packages;
3. run code in Chapter_i.ipynb, data will be saved in Chapter_i/data folder.

Please submit an issue or send an email to [email protected], if you have any questions or suggestions.