Theoretical Modeling of the Iterative Properties of User Discovery in a Collaborative Filtering Recommender System

1 code implementation • 21 Aug 2020 • Sami Khenissi, Mariem Boujelbene, Olfa Nasraoui

We present a theoretical framework to model the asymptotic evolution of the different components of a recommender system operating within a feedback loop setting, and derive theoretical bounds and convergence properties on quantifiable measures of the user discovery and blind spots.

BIG-bench Machine Learning Collaborative Filtering +1