Recommender Systems

Module 1: Recommender Systems and where to find them
– Google
– Ads
– Netflix

Module 2: Basic Recommender Systems
– Cosine Distance
– SVD
– SVD for recommendation system factorization
Hands-on Lab:
– Implement Singular Value Decomposition
– Understand how it affects the Cosine Distance

Module 3: Candidate Generation
– Content Based Filtering
– Collaborative Filtering
– Matrix Factorization
Hands-on Lab: Use the studied matrix factorization techniques to implement a basic recommendation system and benchmark the factorization methods

Module 4: Recommender using Deep Neural Networks
– Softmax Model
– Softmax Embedding
– Embeddings for Neural Networks
– Item2Vec
Hands-on Lab: Build a recommendation system using neural networks embeddings for IMDB movies

Module 5: Ranking
– Retrieval
– Scoring
– Re-ranking
Hands-on Lab:
– Learn how to improve your previous model by understanding how the movie ranking is made
– Implement your Collaborative Filtering and Hybrid Collaborative Filtering methods to make a better recommendation system

Module 6: Autoencoder for Recommender Systems
– Autoencoders
– Latent Spaces
– Variational Autoencoders
Hands-on Lab: Build a recommendation system for retail stores using Autoencoders