Pawan Chorasiya

This research presents a transformer-based approach for Next Basket Recommendation (NBR) in grocery retail, focusing on predicting new, previously unpurchased products. Using a modified T5 architecture, the model captures item-to-item correlations across sequential shopping baskets, framing NBR as a sequence-to-sequence task. Extensive evaluations on the Dunnhumby dataset show that the model outperforms traditional collaborative filtering methods, achieving high precision and ranking effectiveness. This approach demonstrates the potential of transformers for personalized and novel product recommendations.