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

PORTFOLIO

GITHUB

BLOG