The recommender problem revisited: morning tutorial

In 2006, Netflix announced a $1M prize competition to advance recommendation algorithms. The recommendation problem was simplified as the accuracy in predicting a user rating measured by the Root Mean Squared Error. While that formulation helped get the attention of the research community in the area, it may have put an excessive focus on what is simply one of possible approaches to recommendations. In this tutorial we will describe different components of modern recommender systems such as: personalized ranking, similarity, explanations, context-awareness, or search as recommendation. In the first part, we will use the Netflix use case as a driving example of a prototypical industrial-scale recommender system. We will also review the usage of modern algorithmic approaches that include algorithms such as Factorization Machines, Restricted Boltzmann Machines, SimRank, Deep Neural Networks, or Listwise Learning-to-rank. In the second part, we will focus on the area of context-aware recommendations where the two dimensional user-item recommender problem is turned into an n-dimensional space.