Neuroscientific User Models: The Source of Uncertain User Feedback and Potentials for Improving Web Personalisation

TL;DR

This paper explores how neuroscientific theories, particularly the Bayesian brain, can inform adaptive web systems by modeling neural decision-making processes to better understand and utilize user feedback variability for improved personalization.

Contribution

It introduces a neural-based cognitive model and decoder functions to interpret user decision-making, aiming to enhance web personalization through neural user clustering.

Findings

Neural mechanisms of decision-making can be modeled for web personalization.

Variability in user behavior can be exploited for better personalization.

Neural user models enable clustering based on neural characteristics.

Abstract

In this paper we consider the neuroscientific theory of the Bayesian brain in the light of adaptive web systems and content personalisation. In particular, we elaborate on neural mechanisms of human decision-making and the origin of lacking reliability of user feedback, often denoted as noise or human uncertainty. To this end, we first introduce an adaptive model of cognitive agency in which populations of neurons provide an estimation for states of the world. Subsequently, we present various so-called decoder functions with which neuronal activity can be translated into quantitative decisions. The interplay of the underlying cognition model and the chosen decoder function leads to different model-based properties of decision processes. The goal of this paper is to promote novel user models and exploit them to naturally associate users to different clusters on the basis of their individual neural characteristics and thinking patterns. These user models might be able to turn the variability of user behaviour into additional information for improving web personalisation and its experience.