Industrial and Management Engineering, IIT Kanpur, Kanpur, UP, India.
Muma College of Business, University of South Florida, Tampa, FL, United States of America.
PLoS One. 2021 Jan 7;16(1):e0245096. doi: 10.1371/journal.pone.0245096. eCollection 2021.
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.
算法在决定向在线用户展示哪些新闻文章方面的作用日益凸显。最近，这些系统产生了一些不良后果，包括容易放大细微差异，减少读者的选择。在本文中，我们提出并研究了一类新的反馈模型，这类模型表现出多种自组织行为。除了展现重要的突现特性外，我们的模型以一种为媒体管理者提供引导自组织动态的突现结果的机制，对流行的“热门新闻推荐系统”进行了推广。我们使用复杂自适应系统框架来对新闻文章的流行度演化进行建模。具体来说，我们使用基于代理的模拟在微观层面上对读者的行为进行建模，并研究了各种模拟超参数对整体突现现象的影响。这种模拟实验使我们能够展示反馈模型如何可用作传统热门-N 系统的替代推荐器。最后，我们提出了一个用于多目标进化优化的设计框架，使推荐系统能够与不断变化的在线新闻阅读环境共同进化。
