A complex systems perspective of news recommender systems: Guiding emergent outcomes with feedback models.

ABSTRACT: Algorithms are increasingly making decisions regarding what news articles should be shown to online users. In recent times, unhealthy outcomes from these systems have been highlighted including their vulnerability to amplifying small differences and offering less choice to readers. In this paper we present and study a new class of feedback models that exhibit a variety of self-organizing behaviors. In addition to showing important emergent properties, our model generalizes the popular "top-N news recommender systems" in a manner that provides media managers a mechanism to guide the emergent outcomes to mitigate potentially unhealthy outcomes driven by the self-organizing dynamics. We use complex adaptive systems framework to model the popularity evolution of news articles. In particular, w

SUBMITTER: Prawesh S 

PROVIDER: S-EPMC7790545 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature