Intra-Category Multi-Choice Preferences Learning and Assortment Recommendation in E-Commerce

This paper proposes a framework for learning customer preferences and optimizing e-commerce assortments, focusing on intra-category multi-choice behavior, where customers buy multiple items within the same category. Unlike traditional discrete choice models (DCMs) that assume single-product purchases, e-commerce data show frequent intra-category multiproduct purchases, especially during promotions. To capture this, we introduce the multi-choice rank list model (MC-RLM), which accounts for both multi-purchase and substitution effects. Each customer type is defined by a preference ranking and an intended purchase quantity (IPQ), allowing selection of up to IPQ products. The MC-RLM adheres to the regularity axiom and aligns with random utility theory. We present the multi-choice market discovery algorithm to estimate the MC-RLM, extending single-purchase methods to multi-purchase settings. We also introduce behavior-reveal-preference (BRP) rules, using customer behavior data (e.g., clicks, cart additions) to enhance preference estimation. Given the NP-hardness of the assortment optimization problem, we analyze the performance of the revenue-ordered assortments heuristic and provide guarantees. The problem is formulated as a mixed-integer linear program that can generate personalized recommendations based on real-time customer data. Extensive numerical experiments, including a case study using Tmall data, demonstrate that the MC-RLM outperforms models such as the independent choice model and the multi-purchase multinomial logit model in predictive accuracy, with BRP rules further enhancing performance. Synthetic experiments confirm that accurately modeling multi-purchase behavior significantly boosts expected revenue.