Low-Rank Online Dynamic Assortment with Dual Contextual Information

arXiv:2404.17592v1 Announce Type: cross
Abstract: As e-commerce expands, delivering real-time personalized recommendations from vast catalogs poses a critical challenge for retail platforms. Maximizing revenue requires careful consideration of both individual customer characteristics and available item features to optimize assortments over time. In this paper, we consider the dynamic assortment problem with dual contexts -- user and item features. In high-dimensional scenarios, the quadratic growth of dimensions complicates computation and estimation. To tackle this challenge, we introduce a new low-rank dynamic …

abstract arxiv challenge commerce cs.ir cs.lg customer dynamic e-commerce features information low paper personalized personalized recommendations platforms real-time recommendations retail revenue stat.ml type vast