A Hybrid Rule-Augmented Stacked Ensemble Framework for Market Basket Completion with Leak-Free Customer History

Predicting the next item a customer is likely to add to a partially filled shopping basket is a highly actionable sub-problem of recommendation, but existing approaches either rely on association rules that overstate confidence for small itemsets and ignore customer segment differences, or on machine-learning classifiers that discard the interpretable statistical structure of transactional data. This paper presents a hybrid rule-augmented framework for market basket completion that mines association rules separately per country-and-season segment and corrects them using Wilson-Score Adjusted Confidence (WSAC), Segment-Normalized Lift (SN-Lift), and a cross-fold rule stability score, all computed under a strictly out-of-fold protocol to prevent leakage. These rule features are combined with a leak-free customer purchase history feature and fed into six architecturally diverse base classifiers, fused through a stacked logistic regression meta-model. On the UCI Online Retail dataset (541,909 transaction rows, 15,619 baskets), the full pipeline lifts accuracy from an 80% majority-class floor to 87.3%, with the stacked model achieving the best overall AUC of 0.855. Customer purchase history alone is the single strongest signal, present in 42.3%
of correct predictions against 6.9% of incorrect ones. Evaluation uses Lift-over-Floor and Hit@K ranking metrics with 10-fold time-series cross-validation and paired bootstrap testing over held-out baskets, and confirms every reported gain is statistically significant (p < 0.05). Results show that segment-aware, leakage-free feature engineering, not classifier complexity, drives the performance gain, with all six base classifiers converging to a tight 0.869–0.873 accuracy band, and residual error concentrates in the
sparsest data segments rather than being distributed uniformly.