Click-through rate (CTR) prediction is a critical task for e-commerce advertising systems. However, industrial ranking models still rely on sparse IDs and historical behavior signals, which limits their generalization to cold-start products, long-tail queries, and complex user intents. Although large language models (LLMs) provide strong semantic understanding and can generate behavioral rationales, directly using them for online ranking is impractical under strict latency constraints.

We propose GRAM-V2, a rationale-supervised ranking framework that transfers LLM-generated behavioral rationales into an efficient CTR model. GRAM-V2 uses a Teacher LLM to generate click-oriented rationales, distills them into a lightweight Student LLM, and aligns a two-tower CTR model with the Student’s representations and rationale signals. During online serving, GRAM-V2 uses only distilled representations and cached item embeddings, without generating rationales autoregressively. Large-scale offline experiments and online A/B testing show that GRAM-V2 improves CTR prediction accuracy, CTR, and advertising revenue while satisfying production latency constraints.

Query classification, including multiple subtasks such as intent and category prediction, is a vital part of e-commerce applications. E-commerce queries are usually short and lack context, and the information between labels cannot be used, resulting in insufficient prior information for modeling. Most existing industrial query classification methods rely on users’ posterior click behavior to construct training samples, resulting in a Matthew vicious cycle. Furthermore, the subtasks of query classification lack a unified framework, leading to low efficiency for algorithm improvement.In this paper, we propose a novel Semi-supervised Scalable Unified Framework (SSUF), containing multiple enhanced modules to unify the query classification tasks. The knowledge-enhanced module uses world knowledge to enhance query representations and solve the problem of insufficient query information. The label-enhanced module uses label semantics and semi-supervised signals to reduce the dependence on posterior labels. The structure-enhanced module enhances the label representation based on the complex label relations. Each module is highly pluggable, and input features can be added or removed as needed according to each subtask. We conduct extensive offline and online A/B experiments, and the results show that SSUF significantly outperforms the state-of-the-art models.