An Interpretable Data-Driven Unsupervised Approach for the Prevention of Forgotten Items

TL;DR

This paper introduces an interpretable, data-driven approach to identify forgotten items in supermarket shopping, addressing a gap in existing Next Basket Prediction methods by providing transparent explanations and outperforming current baselines.

Contribution

It proposes two novel algorithms specifically designed to detect forgotten items with human-understandable explanations, advancing interpretability in retail prediction models.

Findings

Algorithms outperform state-of-the-art NBP baselines by 10-15%

Methods provide intuitive explanations for recommendations

Approach validated on real-world retail data

Abstract

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.