Dynamic Learning-to-Rank (DLTR) is a method of updating a ranking policy in real-time based on user feedback, which may not always be accurate. Although previous DLTR work has achieved fair and unbiased DLTR under inaccurate feedback, they face the trade-off between fairness and user utility and also have limitations in the setting of feeding items. Existing DLTR works improve ranking utility by eliminating bias from inaccurate feedback on observed items, but the impact of another pervasive form of inaccurate feedback, overlooked or ignored interesting items, remains unclear. For example, users may browse the rankings too quickly to catch interesting items or miss interesting items because the snippets are not optimized enough. This phenomenon raises two questions: i) Will overlooked interesting items affect the ranking results? ii) Is it possible to improve utility without sacrificing fairness if these effects are eliminated? These questions are particularly relevant for small and medium-sized retailers who are just starting out and may have limited data, leading to the use of inaccurate feedback to update their models. In this paper, we find that inaccurate feedback in the form of overlooked interesting items has a negative impact on DLTR performance in terms of utility. To address this, we treat the overlooked interesting items as noise and propose a novel DLTR method, the Co-teaching Rank (CoTeR), that has good utility and fairness performance when inaccurate feedback is present in the form of overlooked interesting items. Our solution incorporates a co-teaching-based component with a customized loss function and data sampling strategy, as well as a mean pooling strategy to further accommodate newly added products without historical data. Through experiments, we demonstrate that CoTeRx not only enhances utilities but also preserves ranking fairness, and can smoothly handle newly introduced items.

动态学习排名（DLTR）是一种根据用户反馈实时更新排名策略的方法，该方法可能并不总是准确的。尽管之前的DLTR工作在不准确的反馈下实现了公平、公正的DLTR，但它们面临着公平性和用户效用之间的权衡，并且在喂养项的设置上也存在局限性。现有的 DLTR 工作通过消除对观察到的项目的不准确反馈带来的偏差来提高排名效用，但另一种普遍形式的不准确反馈、被忽视或忽略的有趣项目的影响仍不清楚。例如，用户可能会过快地浏览排名而无法捕捉到感兴趣的项目，或者由于片段优化不够而错过感兴趣的项目。这种现象提出了两个问题：i）被忽视的有趣项目会影响排名结果吗？ ii)如果消除这些影响，是否有可能在不牺牲公平性的情况下提高效用？这些问题对于刚刚起步且可能数据有限的中小型零售商尤其重要，导致使用不准确的反馈来更新模型。在本文中，我们发现以被忽视的有趣项目形式出现的不准确反馈会对 DLTR 的效用性能产生负面影响。为了解决这个问题，我们将被忽视的兴趣项目视为噪音，并提出了一种新颖的 DLTR 方法，即协同教学排名（CoTeR），当不准确的反馈以被忽视的兴趣项目的形式存在时，该方法具有良好的实用性和公平性。我们的解决方案结合了基于协同教学的组件，具有定制的损失函数和数据采样策略，以及均值池策略，以进一步适应没有历史数据的新添加的产品。通过实验，我们证明 CoTeRx 不仅增强了实用性，还保持了排名公平性，并且可以顺利处理新引入的​​项目。