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 tradeoff 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? and (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 article, 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 CoTeR not only enhances utilities but also preserves ranking fairness and can smoothly handle newly introduced items.

Machine unlearning requires removing the information of forgetting data while keeping the necessary information of remaining data. Despite recent advancements in this area, existing methodologies mainly focus on the effect removal of forgetting data without considering the negative impact this can have on the information of the remaining data, resulting in significant performance degradation after data removal. Although some methods try to repair the performance of remaining data after removal, the forgotten information can also return after repair. Such an issue is due to the intricate intertwining of the forgetting and remaining data. Without adequately differentiating the influence of these two kinds of data on the model, existing algorithms take the risk of either inadequate removal of the forgetting data or unnecessary loss of valuable information from the remaining data. To address this shortcoming, the present study undertakes a causal analysis of the unlearning and introduces a novel framework termed Causal Machine Unlearning (CaMU). This framework adds intervention on the information of remaining data to disentangle the causal effects between forgetting data and remaining data. Then CaMU eliminates the causal impact associated with forgetting data while concurrently preserving the causal relevance of the remaining data. Comprehensive empirical results on various datasets and models suggest that CaMU enhances performance on the remaining data and effectively minimizes the influences of forgetting data. Notably, this work is the first to interpret deep model unlearning tasks from a new perspective of causality and provide a solution based on causal analysis, which opens up new possibilities for future research in deep model unlearning.