Bayesian Brain meets Bayesian Recommender - Towards Systems with Empathy for the Human Nature

TL;DR

This paper explores integrating the Bayesian brain theory from neuroscience into recommender systems, highlighting how biological insights into noisy user feedback and multicomponent models can enhance understanding and prediction of human behavior.

Contribution

It demonstrates the relevance of cognitive neuroscience concepts, particularly the Bayesian brain, for improving recommender systems by modeling complex human feedback.

Findings

Noisy user feedback impacts recommendation accuracy

Multicomponent user models reflect biological origins

Experimental results support neuroscience-inspired approaches

Abstract

In this paper we consider the modern theory of the Bayesian brain from cognitive neurosciences in the light of recommender systems and expose potentials for our community. In particular, we elaborate on noisy user feedback and the thus resulting multicomponent user models, which have indeed a biological origin. In real user experiments we observe the impact of both factors directly in a repeated rating task along with recommendation. As a consequence, this contribution supports the plausibility of contemporary theories of mind in the context of recommender systems and can be understood as a solicitation to integrate ideas of cognitive neurosciences into our systems in order to further improve the prediction of human behaviour.