The goal of a next basket recommendation (NBR) system is to recommend items for the next basket for a user, based on the sequence of their prior baskets. Recently, a number of methods with complex modules have been proposed that claim state-of-the-art performance. They rarely look into the predicted basket and just provide intuitive reasons for the observed improvements, e.g., better representation, capturing intentions or relations, etc. We provide a novel angle on the evaluation of next basket recommendation methods, centered on the distinction between repetition and exploration: the next basket is typically composed of previously consumed items (i.e., repeat items) and new items (i.e, explore items). We propose a set of metrics that measure the repeat/explore ratio and performance of NBR models. Using these new metrics, we analyze state-of-the-art NBR models. The results of our analysis help to clarify the extent of the actual progress achieved by existing NBR methods as well as the underlying reasons for the improvements. Overall, our work sheds light on the evaluation problem of NBR and provides useful insights into the model design for this task.

翻译：下一篮推荐（NBR）系统的目标是根据用户先前购物篮的序列，为其下一篮推荐物品。近年来，大量具有复杂模块的方法被提出，声称实现了最先进的性能。这些方法很少深入探究预测的购物篮，仅为所观察到的改进提供直观理由，例如更好的表示、捕捉意图或关系等。我们提出了一种评估下一篮推荐方法的新视角，核心在于区分重复与探索：下一个购物篮通常由先前消费过的物品（即重复物品）和新物品（即探索物品）组成。我们提出了一套衡量NBR模型重复/探索比率及性能的指标。利用这些新指标，我们分析了最先进的NBR模型。分析结果有助于阐明现有NBR方法实际进展的程度，以及改进背后的根本原因。总体而言，我们的工作揭示了NBR的评估问题，并为该任务的模型设计提供了有价值的见解。