Recommender systems have been widely used in e-commerce, and re-ranking models are playing an increasingly significant role in the domain, which leverages the inter-item influence and determines the final recommendation lists. Online learning methods keep updating a deployed model with the latest available samples to capture the shifting of the underlying data distribution in e-commerce. However, they depend on the availability of real user feedback, which may be delayed by hours or even days, such as item purchases, leading to a lag in model enhancement. In this paper, we propose a novel extension of online learning methods for re-ranking modeling, which we term LAST, an acronym for Learning At Serving Time. It circumvents the requirement of user feedback by using a surrogate model to provide the instructional signal needed to steer model improvement. Upon receiving an online request, LAST finds and applies a model modification on the fly before generating a recommendation result for the request. The modification is request-specific and transient. It means the modification is tailored to and only to the current request to capture the specific context of the request. After a request, the modification is discarded, which helps to prevent error propagation and stabilizes the online learning procedure since the predictions of the surrogate model may be inaccurate. Most importantly, as a complement to feedback-based online learning methods, LAST can be seamlessly integrated into existing online learning systems to create a more adaptive and responsive recommendation experience. Comprehensive experiments, both offline and online, affirm that LAST outperforms state-of-the-art re-ranking models.

翻译：推荐系统在电子商务领域已得到广泛应用，重排序模型在该领域扮演着日益重要的角色，其通过利用商品间的相互影响来决定最终的推荐列表。在线学习方法通过持续使用最新可用样本更新已部署模型，以捕捉电子商务中底层数据分布的动态变化。然而，这类方法依赖于真实用户反馈的可用性，而此类反馈（如商品购买行为）可能存在数小时甚至数天的延迟，导致模型增强滞后。本文提出一种用于重排序建模的在线学习方法新扩展，我们称之为LAST（服务时学习）。该方法通过使用替代模型提供指导信号来驱动模型改进，从而规避了对用户反馈的依赖。在接收到在线请求时，LAST会在生成该请求的推荐结果前实时查找并应用模型修改。这种修改具有请求特定性和瞬时性：它专为且仅针对当前请求定制，以捕捉该请求的特定上下文；请求处理后修改即被丢弃，这有助于防止错误传播并稳定在线学习过程，因为替代模型的预测可能存在偏差。最重要的是，作为基于反馈的在线学习方法的补充，LAST可以无缝集成到现有在线学习系统中，以创建更具适应性和响应性的推荐体验。离线和在线的综合实验均证实，LAST优于当前最先进的重排序模型。