FlySCoRe: Fly-Inspired Sparse Cohort Retrieval for Next-Basket Recommendation

Next-basket recommendation ranks the products that a household is likely to purchase on its next shopping occasion. The task combines recurring patterns in the household's own history with purchase evidence shared across households. We introduce Fly-inspired Sparse Cohort Retrieval (FlySCoRe), a retrieval-based ranking model inspired by the sparse associative organization of the Drosophila mushroom body. For each history–candidate pair, a history-conditioned user signal summarizes purchasing behavior across time scales, while a sparse address selects memory locations containing weighted purchase and opportunity evidence from earlier observations. A support-aware readout combines the retrieved evidence with the user signal to rank the full product catalog. We instantiate this design in three ways. A central nervous system model (CNS) couples learned task interfaces to fixed measured connectivity; a probabilistic model (PROB) expresses memory selection through choice probabilities; and a neural model (NET) uses learned activation strengths. Across four repeat-purchase datasets, the highest-scoring implementation exceeds the strongest of eleven baselines in NDCG@20 by 4.81%-33.60%. We further construct an alternative memory interface from retained input activity and composed connections. Rebuilding historical memory at this interface preserves 97.0%-99.3% of complete CNS NDCG@20. These results support sparse cohort retrieval across measured, probabilistic, and neural implementations.