论文标题
推荐系统中偏好操纵的解决方案需要了解元优先
Solutions to preference manipulation in recommender systems require knowledge of meta-preferences
论文作者
论文摘要
用于为推荐系统供电的迭代机器学习算法通常通过尝试学习来改变人们的偏好。此外，推荐人可以通过使用户更具可预测性来更好地预测用户将做什么。用户的某些偏好更改是自我诱导的，无论推荐人是否引起了他们的要求。本文提出，建议系统中的偏好操纵解决方案必须考虑到某些元优率（偏爱另一个偏好），以尊重用户的自主权而不是操纵。
Iterative machine learning algorithms used to power recommender systems often change people's preferences by trying to learn them. Further a recommender can better predict what a user will do by making its users more predictable. Some preference changes on the part of the user are self-induced and desired whether the recommender caused them or not. This paper proposes that solutions to preference manipulation in recommender systems must take into account certain meta-preferences (preferences over another preference) in order to respect the autonomy of the user and not be manipulative.
