AI RESEARCH
Learning in Position-Aware Multinomial Logit Bandits: From Multiplicative to General Position Effects
arXiv CS.LG
•
ArXi:2605.17238v1 Announce Type: new We study the dynamic joint assortment selection and positioning problem, where the attraction of each product depends on both its intrinsic appeal and its display position under a Multinomial Logit (MNL) choice framework. Our study ranges from the multiplicative position effects model, in which each product's attraction is scaled by a position-specific factor, to a general position effects model assigning independent attraction parameters to every product--position pair to capture heterogeneous synergies.