Facebook paper 2019

Deep Learning Recommendation Model for Personalization and Recommendation Systems

Two primary perspective

- Recommendation systems
- Content filtering → collaborative filtering → Neighborhood method

- Predictive analytics
- Statistical models to classify or predict the probability of events
- From simple regression to models with deep networks

- With embedding,which transform one-hot vectors into dense representations

- Statistical models to classify or predict the probability of events

Propose a personalization model with such two perspectives

- Uses embeddings to process sparse features into dense features
- Interacts these features using the statistical techinques (Factorizaion Machine)
- Finds the event probability with MLP

- An embedding is a mapping of a discrete variable to a vector of continuous numbers
- Embeddings map each category to a dense representation in an abstract space
- One-hot or multi-hot vector to dense representation

- Dot-product of two shows similiarity

- Verification of embeddings

MF is a simple embedding model — google developer page