Recommender systems are central to modern online platforms, but a popular concern is that they may be pulling society in dangerous directions (e.g., towards filter bubbles). However, a challenge with measuring the effects of recommender systems is how to compare user outcomes under these systems to outcomes under a credible counterfactual world without such systems. We take a model-based approach to this challenge, introducing a dichotomy of process models that we can compare: (1) a "recommender" model describing a generic item-matching process under a personalized recommender system and (2) an "organic" model describing a baseline counterfactual where users search for items without the mediation of any system. Our key finding is that the recommender and organic models result in dramatically different outcomes at both the individual and societal level, as supported by theorems and simulation experiments with real data. The two process models also induce different trade-offs during inference, where standard performance-improving techniques such as regularization/shrinkage have divergent effects. Shrinkage improves the mean squared error of matches in both settings, as expected, but at the cost of less diverse (less radical) items chosen in the recommender model but more diverse (more radical) items chosen in the organic model. These findings provide a formal language for how recommender systems may be fundamentally altering how we search for and interact with content, in a world increasingly mediated by such systems.
翻译:建议者系统是现代在线平台的核心,但公众关切的是,它们可能将社会拉向危险的方向(例如,向过滤泡泡),但衡量建议者系统影响的挑战是如何将这些系统下的用户结果与没有这种系统的可信反事实世界下的结果进行比较。我们采用基于模型的方法来应对这一挑战,引入一种我们可比较的程序模型的二分法:(1) “共鸣”模式,描述个人化建议系统下的通用项目匹配程序,(2) “有机”模式,描述用户在不经过任何系统调解的情况下寻找项目的基线对应事实。然而,我们的关键发现是,建议者和有机模式在个人和社会层面导致显著不同的结果,得到理论支持,并用真实数据的模拟实验。两种进程模型还引发了不同的判断,在这种推论中,标准性改进技术,如规范/缩小效果不同。 缩小使两种环境中的匹配平均对称错误得到改善,如预期的那样,但以较不易多样化的(不彻底的)媒体模型中选择的模型成本为越来越低。