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
Deep Recommender Libraries
1.Tensorrec - Built on Tensorflow
2.Spotlight - Built on PyTorch
3.TFranking - Built on TensorFlow (Learning to Rank)
Matrix Factorisation Based Libraries
1.Implicit - Implicit Matrix Factorisation
2.QMF - Implicit Matrix Factorisation
3.Lightfm - For Hybrid Recommedations
4.Surprise - Scikit-learn type api for traditional alogrithms
Similarity Search Libraries
1.Annoy - Approximate Nearest Neighbour
2.NMSLib - kNN methods
3.FAISS - Similarity search and clustering
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
Piyush Pathak
