A Transformer-Based Choice Model for Multiple Choice with Context and Basket Effects

Modeling multiple-item purchases is important for retail decisions, but challenging because the number of possible baskets grows exponentially with the number of items, and item interactions may depend on both the offered assortment and the partially formed basket. We introduce Cross-Effects Transformer (XTra), a sequential choice model that decomposes item utility into intrinsic utility, assortment context interactions, and basket interactions. XTra derives explicit item-to-item interaction scores from contextualized representations, allowing pairwise relationships to vary with the surrounding assortment and basket. On the Visuelle 2.0 retail dataset, XTra improves test unordered basket negative log-likelihood from 11.039 for the closest Transformer baseline to 10.243 and a smaller XTra model achieves 10.267 with approximately half as many parameters as the baseline. XTra can utilize pretrained multimodal item representations to generalize to infrequently observed items and its learned interaction rankings are stable across training runs and correlate with held-out co-purchase associations. These results provide a predictive foundation for studying context-dependent assortment and basket decisions.