AI Framework Boosts Cold-Start Prediction in E-commerce.

A new AI framework, SemRaD (Semantic Reasoning-aware Distillation), has been introduced to tackle the persistent challenge of cold-start prediction for new users in e-commerce platforms. This framework aims to accurately predict user lifetime value (LTV) and conversion rates (CVR) even when interaction history is minimal. It addresses limitations of previous approaches, such as the noisiness of LLM-generated rationales and the fragility of naive student-teacher distillation due to information asymmetry. SemRaD features a Structured Semantic Reasoning Pipeline that transforms free-form LLM rationales into a structured schema. This process generates a "Densified Semantic Profile" for each user, which is then consumed by a deployed student model via a Semantic-Gated Encoder, focusing on the most informative dimensions. Additionally, a "Hindsight Distillation Target," reconciled from pre- and post-conversion reasoning, is used during training. To effectively bridge the information gap between the privileged teacher model and the sparse student model, and to account for user-specific variability, SemRaD incorporates a Hindsight-Aware Distillation Network with "Distillation Experts." This comprehensive approach has demonstrated significant improvements on a large-scale industrial dataset, yielding a +1.9% LTV (Gini) and +1.0% CVR (AUROC) over a production baseline. Online A/B testing further confirmed these gains, highlighting SemRaD's practical efficacy in real-world e-commerce scenarios.