论文标题
通过避免反馈循环来公平个性化
Towards Fair Personalization by Avoiding Feedback Loops
论文作者
论文摘要
自我增强反馈回路既是交互式推荐系统中某些内容的过度和/或不足的原因和影响。这导致了错误的用户偏好估计值，即高估了过度呈现内容的同时侵犯了每个替代方案的权利，相反，我们将其定义为公平的系统。我们考虑两个明确合并或忽略系统性和有限替代品的模型。通过模拟，我们证明，忽略系统的演示文稿高估了促进的选择，低估了审查的替代方案。仅根据有限的暴露条件来解决这些偏见的一种补救措施。
Self-reinforcing feedback loops are both cause and effect of over and/or under-presentation of some content in interactive recommender systems. This leads to erroneous user preference estimates, namely, overestimation of over-presented content while violating the right to be presented of each alternative, contrary of which we define as a fair system. We consider two models that explicitly incorporate, or ignore the systematic and limited exposure to alternatives. By simulations, we demonstrate that ignoring the systematic presentations overestimates promoted options and underestimates censored alternatives. Simply conditioning on the limited exposure is a remedy for these biases.
