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This paper introduces a scalable approach for estimating Demand Transfer (DT) coefficients in large item universes using restricted logit modeling, improving demand forecasting for store assortment optimization. Experiments show accurate DT coefficient estimation when substitution behavior assumptions hold.
Proposes a model-agnostic framework for assortment optimization using guided discrete diffusion, representing assortments as binary vectors and using reward-guided reverse diffusion to avoid combinatorial enumeration. Shows robustness and high-quality solutions in high-dimensional settings.
This paper proposes a framework for Markov chain choice models with panel data, including estimation via novel EM algorithms that leverage partial-ordering preference information, personalized choice prediction, and assortment optimization. Experimental results on synthetic data and the sushi dataset show improvements over traditional methods.