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
• AI Conference 2019: WhiteBoard Notes | In-Class Notebooks

Notebooks

• Movies - Movielens

  • 01-Acquire
  • 02-Augment
  • 03-Refine
  • 04-Transform
  • 05-Evaluation
  • 06-Model-Baseline
  • 07-Feature-extractor
  • 08-Model-Matrix-Factorization
  • 09-Model-Matrix-Factorization-with-Bias
  • 10-Model-MF-NNMF
  • 11-Model-Deep-Matrix-Factorization
  • 12-Model-Neural-Collaborative-Filtering
  • 13-Model-Implicit-Matrix-Factorization
  • 14-Features-Image
  • 15-Features-NLP

• Ecommerce - YooChoose

  • 01-Data-Preparation
  • 02-Models

• News - Hackernews

• Product - Groceries

Python Libraries

Deep Recommender Libraries

• Tensorrec - Built on Tensorflow
• Spotlight - Built on PyTorch
• TFranking - Built on TensorFlow (Learning to Rank)

Matrix Factorisation Based Libraries

• Implicit - Implicit Matrix Factorisation
• QMF - Implicit Matrix Factorisation
• Lightfm - For Hybrid Recommedations
• Surprise - Scikit-learn type api for traditional alogrithms

Similarity Search Libraries

• Annoy - Approximate Nearest Neighbour
• NMSLib - kNN methods
• FAISS - Similarity search and clustering

Content-based Libraries

• Cornac - Leverage Auxiliary Data (Images, Text, Social Networks)

Learning Resources

Reference Slides

• Deep Learning in RecSys by Balázs Hidasi
• Lessons from Industry RecSys by Xavier Amatriain
• Architecting Recommendation Systems by James Kirk
• Recommendation Systems Overview by Raimon and Basilico

Benchmarks

• MovieLens Benchmarks for Traditional Setup
• Microsoft Tutorial on Recommendation System at KDD 2019

Algorithms & Approaches

• Collaborative Filtering for Implicit Feedback Datasets
• Bayesian Personalised Ranking for Implicit Data
• Logistic Matrix Factorisation
• Neural Network Matrix Factorisation
• Neural Collaborative Filtering
• Variational Autoencoders for Collaborative Filtering

Evaluations

• Evaluating Recommendation Systems