SML: Recommender Systems

Content

• Neighborhood methods

  • User / movie similarity

  • Iteration on graph

• Matrix Factorization

  • Singular value decomposition

  • Convex reformulation

• Ranking and Session Modeling

  • Ordinal regression

  • Session models

• Features

  • Latent dense (Bayesian Probabilistic Matrix Factorization)

  • Latent sparse (Dirichlet process factorization)

  • Coldstart problem (inferring features)

• Hashing

Supplementary material

Slides in PDF and Keynote. If you want to extract the equations from the slides you can do so by using LaTeXit, simply by dragging the equation images into it.

Videos

This is unedited video straight from a Lumix GF2 with a 20mm lens which should explain the sound (it doesn't have a dedicated audio input) … But it should help as a supplement with the slides (YouTube typically makes the 1080i version available within 1 week of the upload).