提出可解释的无监督方法，精准识别购物遗漏商品并给出合理解释。
An Interpretable Data-Driven Unsupervised Approach for the Prevention of Forgotten Items
- 设计可解释算法，主动检测未购商品
- 在真实零售数据上提升10-15%预测性能
- 适合需要透明推荐的智能购物场景
准确识别超市购物中被遗忘的商品，并提供清晰可理解的推荐理由，仍是下一篮子预测（Next Basket Prediction, NBP）领域中的未充分探索问题。现有NBP方法通常仅关注未来购买预测，未明确处理无意遗漏商品的检测。这一空白部分源于缺乏能可靠估计遗忘商品的真实世界数据集。此外，多数现有NBP方法依赖黑箱模型，缺乏透明性，难以向用户解释推荐依据。本文首次正式提出遗忘商品预测任务，并提出两种面向可解释性的新算法。这些方法专为识别遗忘商品并提供直观、人类可理解的解释而设计。在真实零售数据集上的实验表明，我们的算法在多个评估指标上优于当前最先进的NBP基线方法，性能提升达10%-15%。
原文摘要 · Abstract (English)
Accurately identifying items forgotten during a supermarket visit and providing clear, interpretable explanations for recommending them remains an underexplored problem within the Next Basket Prediction (NBP) domain. Existing NBP approaches typically only focus on forecasting future purchases, without explicitly addressing the detection of unintentionally omitted items. This gap is partly due to the scarcity of real-world datasets that allow for the reliable estimation of forgotten items. Furthermore, most current NBP methods rely on black-box models, which lack transparency and limit the ability to justify recommendations to end users. In this paper, we formally introduce the forgotten item prediction task and propose two novel interpretable-by-design algorithms. These methods are tailored to identify forgotten items while offering intuitive, human-understandable explanations. Experiments on a real-world retail dataset show our algorithms outperform state-of-the-art NBP baselines by 10-15% across multiple evaluation metrics.
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