论文标题
评估：推荐系统的全面评估
EvalRS: a Rounded Evaluation of Recommender Systems
论文作者
论文摘要
推荐系统（RSS）的许多复杂性来自以下事实：它们被用作更复杂的应用程序的一部分，并通过各种用户界面来影响用户体验。但是，研究几乎完全关注RSS产生准确的项目排名的能力，同时很少关注对现实世界中RS行为的评估。如此狭窄的重点限制了RSS对现实世界产生持久影响的能力，并使它们容易受到不希望的行为的影响，例如加强数据偏见。我们将评估作为一种新的挑战类型，以促进从业者之间的讨论，并在开放的新方法中建立用于测试“野外” RSS的方法。
Much of the complexity of Recommender Systems (RSs) comes from the fact that they are used as part of more complex applications and affect user experience through a varied range of user interfaces. However, research focused almost exclusively on the ability of RSs to produce accurate item rankings while giving little attention to the evaluation of RS behavior in real-world scenarios. Such narrow focus has limited the capacity of RSs to have a lasting impact in the real world and makes them vulnerable to undesired behavior, such as reinforcing data biases. We propose EvalRS as a new type of challenge, in order to foster this discussion among practitioners and build in the open new methodologies for testing RSs "in the wild".
