Transformer-based approaches such as BERT4Rec and SASRec demonstrate strong performance in Next Item Recommendation (NIR) tasks. However, applying these architectures to Next-Basket Recommendation (NBR) tasks, which often involve highly repetitive interactions, is challenging due to the vast number of possible item combinations in a basket. Moreover, frequency-based methods such as TIFU-KNN and UP-CF still demonstrate strong performance in NBR tasks, frequently outperforming deep-learning approaches. This paper introduces SAFERec, a novel algorithm for NBR that enhances transformer-based architectures from NIR by incorporating item frequency information, consequently improving their applicability to NBR tasks. Extensive experiments on multiple datasets show that SAFERec outperforms all other baselines, specifically achieving an 8\% improvement in Recall@10.

翻译：基于Transformer的方法（如BERT4Rec和SASRec）在下一项推荐任务中表现出色。然而，将这些架构应用于下一篮推荐任务具有挑战性，因为篮中可能的物品组合数量庞大，且此类任务通常涉及高度重复的交互。此外，基于频率的方法（如TIFU-KNN和UP-CF）在下一篮推荐任务中仍展现出强大性能，经常优于深度学习方法。本文提出SAFERec，一种用于下一篮推荐的新型算法，它通过融入物品频率信息来增强源自下一项推荐的Transformer架构，从而提升其在下一篮推荐任务中的适用性。在多个数据集上的大量实验表明，SAFERec优于所有其他基线方法，特别是在Recall@10指标上实现了8%的提升。