论文标题
基于增强学习的推荐系统中的明确用户操纵
Explicit User Manipulation in Reinforcement Learning Based Recommender Systems
论文作者
论文摘要
推荐系统在现代世界中非常普遍，因为它们对雇用它们的用户，平台和服务的价值。通常，他们可以改善用户体验并帮助提高满意度，但没有冒险。这种风险是他们对用户的影响及其在塑造用户偏好中发挥积极作用的能力。这种风险对于基于增强学习的推荐系统更为重要。这些能够学习，例如，今天向用户显示的内容如何损害该用户对将来推荐的其他内容的偏好。因此，基于增强学习的建议系统可以隐式学习影响用户，如果这意味着最大化点击，参与度或消费。特别是在社交新闻和媒体平台上，这种行为引起警报。社交媒体无疑在公众舆论中发挥了作用，已被证明是增加政治两极分化的一个因素。因此，此类平台上的推荐系统具有巨大的潜力，可以以不良的方式影响用户。但是，这种形式的操作也可能被故意使用。随着政治意见动态建模的进步和用户数据的更大收集，用户操作明确，其中用户的信念和意见量身定制了一定端，这是对基于强化学习的推荐系统的重要关注。
Recommender systems are highly prevalent in the modern world due to their value to both users and platforms and services that employ them. Generally, they can improve the user experience and help to increase satisfaction, but they do not come without risks. One such risk is that of their effect on users and their ability to play an active role in shaping user preferences. This risk is more significant for reinforcement learning based recommender systems. These are capable of learning for instance, how recommended content shown to a user today may tamper that user's preference for other content recommended in the future. Reinforcement learning based recommendation systems can thus implicitly learn to influence users if that means maximizing clicks, engagement, or consumption. On social news and media platforms, in particular, this type of behavior is cause for alarm. Social media undoubtedly plays a role in public opinion and has been shown to be a contributing factor to increased political polarization. Recommender systems on such platforms, therefore, have great potential to influence users in undesirable ways. However, it may also be possible for this form of manipulation to be used intentionally. With advancements in political opinion dynamics modeling and larger collections of user data, explicit user manipulation in which the beliefs and opinions of users are tailored towards a certain end emerges as a significant concern in reinforcement learning based recommender systems.
