Abstract

The growing convergence of personalized recommendations and pricing strategies has created new opportunities for retailers to support more targeted promotional decisions and potentially improve revenue-related outcomes, provided that such systems are implemented with appropriate managerial and fairness safeguards. This study introduces a Transformer-driven modeling approach that jointly performs product recommendation and personalized price-level assignment by modeling observed discount-level acceptance as an operational proxy for consumers’ willingness to pay (WTP). Instead of handling price as an auxiliary or fixed attribute, the proposed model learns patterns of observed price responsiveness from sequential purchase histories that integrate user actions, item attributes, and discount exposure. The learning task is cast as a multi-class classification problem with five outcomes—non-purchase and purchases occurring at 0%, 10%, 20%, and above 30% discount levels—enabling the system to recommend not only relevant products but also economically appropriate pricing options. Empirical evaluation on the Dunnhumby retail dataset, which provides rich information on transactions and promotions, shows consistent performance across multiple evaluation criteria, including macro- and micro-level classification metrics, ranking-based measures such as NDCG@5, and Macro averaged AUC. Across the reported metrics, the Transformer-based framework performs better than our earlier deep reinforcement learning approach in this five-class setting, suggesting that sequence-based self-attention can better capture observed behavioral patterns related to discount-level acceptance. By producing integrated product–price recommendations, the proposed framework contributes toward closing the methodological gap between recommendation systems and dynamic pricing, and offers a scalable decision-support framework for data-driven, value-sensitive promotion design in retail and e-commerce contexts, subject to future validation in real-world and fairness-aware implementation settings.

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