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, we use agent-based simulation to model a reader’s behavior at the microscopic level and study the impact of various simulation hyperparameters on overall emergent phenomena. This simulation exercise enables us to show how the feedback model can be used as an alternative recommender to conventional top-N systems. Finally, we present a design framework for multi-objective evolutionary optimization that enables recommendation systems to co-evolve with the changing online news readership landscape.
Highlights
There is growing concern about some undesirable aspects of user interaction with news recommender systems
We present results from running the agent-based model based on the comparison between FNRS and the top-N NRS using the measures defined below
For the results corresponding to different feedback parameters γ, the counts of articles in Display List (DL) were updated in parallel in both mechanisms: FNRS and the top-N system
Summary
There is growing concern about some undesirable aspects of user interaction with news recommender systems (from here on referred to as “NRS”). The overabundance of contents vying for limited attention of users creates a cannibalization effect in social media leading to a “winner take all” effect among news articles [1] where a few articles receive most of the viewership and reader engagement [2]. This can, create and lead to poorly informed societies. The speed with which information could be disseminated on social media creates an opportunity to target recommendation engines for generating clickbait to increase popularity of the target articles [5].
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