An Interpretable Data-Driven Unsupervised Approach for the Prevention of Forgotten Items
This addresses the underexplored issue of forgotten item prediction in retail, providing interpretable recommendations for end users, though it is incremental as it builds on existing NBP methods.
The paper tackles the problem of identifying forgotten items during supermarket visits, proposing interpretable algorithms that outperform state-of-the-art Next Basket Prediction baselines by 10-15% on a real-world retail dataset.
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.