Neuroscientific user models: The source of uncertain user feedback and potentials for improving web personalisation

Abstract

Recent research revealed a considerable lack of reliability for user feedback when interacting with personalisation engines, often denoted as user noise, user variability or human uncertainty. Whenever research on this topic is done, there is a very strong system-centric view in which user variation is something undesirable and should be modelled with the eye to eliminate. However, the possibilities of extracting additional information were only insufficiently considered so far. In this contribution we consider the neuroscientific theory of the Bayesian brain in order to develop novel user models with the power of turning user variability into additional information for improving web personalisation. This will be exemplified by means of standard collaborative filtering.