Next basket recommendation (NBR) is the task of predicting the next set of items based on a sequence of already purchased baskets. It is a recommendation task that has been widely studied, especially in the context of grocery shopping. In next basket recommendation (NBR), it is useful to distinguish between repeat items, i.e., items that a user has consumed before, and explore items, i.e., items that a user has not consumed before. Most NBR work either ignores this distinction or focuses on repeat items. We formulate the next novel basket recommendation (NNBR) task, i.e., the task of recommending a basket that only consists of novel items, which is valuable for both real-world application and NBR evaluation. We evaluate how existing NBR methods perform on the NNBR task and find that, so far, limited progress has been made w.r.t. the NNBR task. To address the NNBR task, we propose a simple bi-directional transformer basket recommendation model (BTBR), which is focused on directly modeling item-to-item correlations within and across baskets instead of learning complex basket representations. To properly train BTBR, we propose and investigate several masking strategies and training objectives: (i) item-level random masking, (ii) item-level select masking, (iii) basket-level all masking, (iv) basket-level explore masking, and (v) joint masking. In addition, an item-basket swapping strategy is proposed to enrich the item interactions within the same baskets. We conduct extensive experiments on three open datasets with various characteristics. The results demonstrate the effectiveness of BTBR and our masking and swapping strategies for the NNBR task. BTBR with a properly selected masking and swapping strategy can substantially improve NNBR performance.

翻译：下一购物篮推荐（NBR）是指根据用户已购买的购物篮序列预测其下一个待购商品组合的任务，这是一项被广泛研究（尤其在杂货购物场景中）的推荐任务。在下一购物篮推荐（NBR）中，区分重复商品（即用户已购买过的商品）与探索商品（即用户未购买过的商品）具有重要意义。现有NBR研究大多忽略此区分或仅聚焦于重复商品。本文提出下一新奇购物篮推荐（NNBR）任务，即推荐仅包含新奇商品的购物篮，该任务对实际应用与NBR评估均具有重要价值。通过评估现有NBR方法在NNBR任务上的表现，我们发现该领域进展有限。为解决NNBR任务，我们提出一种简单的双向变换器购物篮推荐模型（BTBR），该模型专注于直接建模购物篮内部及跨购物篮的商品间关联，而非学习复杂的购物篮表征。为有效训练BTBR，我们提出并研究多种掩码策略与训练目标：（i）商品级随机掩码，（ii）商品级选择性掩码，（iii）购物篮级全掩码，（iv）购物篮级探索掩码，（v）联合掩码。此外，我们提出商品-购物篮交换策略以增强同一购物篮内商品的交互。在三个具有不同特性的公开数据集上的大量实验表明，BTBR模型及其掩码与交换策略在NNBR任务中具有显著有效性。通过合理选择掩码与交换策略，BTBR能够大幅提升NNBR性能。