In this paper we study the next basket recommendation problem. Recent methods use different approaches to achieve better performance. However, many of them do not use information about the time of prediction and time intervals between baskets. To fill this gap, we propose a novel method, Time-Aware Item-based Weighting (TAIW), which takes timestamps and intervals into account. We provide experiments on three real-world datasets, and TAIW outperforms well-tuned state-of-the-art baselines for next-basket recommendations. In addition, we show the results of an ablation study and a case study of a few items.

翻译：本文研究了下一购物篮推荐问题。近期方法采用不同途径以提升性能，但多数方法未利用预测时间及购物篮间的时间间隔信息。为弥补这一不足，我们提出了一种名为时间感知物品加权（TAIW）的新方法，该方法综合考虑时间戳与时间间隔。通过在三个真实世界数据集上的实验，TAIW在下一购物篮推荐任务中优于经过良好调校的现有基准方法。此外，我们还展示了消融实验结果及若干物品的案例研究。