Recommender Systems

Slides in Keynote and PDF.

Content

  • Neighborhood Methods

    • Motivation
    • Nearest Neighbors
    • Nadaraya Watson Style interpolation
    • Bias
    • Similarity between ratings
  • Matrix Factorization

    • Latent variable methods
    • Stochastic Gradient Descent
    • Factors with bias
    • Aldous Hoover Theorem (exchangeability over matrices)
  • Session Modeling

    • Users in context
    • User intent
    • Sequential content consumption (implicit user modeling)
    • Independent clicks, logistic model, sequential clicks, skip-click-view, with context
  • Feature Representation

    • Latent factors from context
    • Cold start problem
    • Multiple sources
    • Homophily
  • Hashing for recommendation

    • Matrix hashing
    • Approximation guarantees
    • Optimization

Supplementary material

Video