Beyond Item-Level Prediction: Fine-Grained CVR Modeling with Price SKU in E-Commerce Recommendation

In large-scale e-commerce platforms, Conversion Rate (CVR) prediction is crucial for recommender system, yet existing approach face a fundamental granularity mismatch: models operate at the item level while users purchase at the fine-grained Stock Keeping Unit (SKU) level. This mismatch causes loss of fine-grained user intent signals. Moreover, it also introduces price inconsistency bias due to the gap between static exposure prices and actual transaction prices. While direct SKU-level modeling would resolve these issues, it is impractical for industrial deployment due to the extreme data sparsity and prohibitive inference costs. To address these challenges, we propose PSKU4Rec, a novel framework that operates at the Price SKU (PSKU) granularity by aggregating SKUs by price to retain critical price signals while reducing sparsity by 6 times. PSKU4Rec consists of: (1) a PSKU-aware prediction network that models intra-item PSKU contextual information and captures user PSKU preferences; and (2) a PSKU-aware application module that generates personalized estimated transaction prices for pCVR refinement and enables personalized main-image display. Offline experiments based on the dataset collected from Taobao App show substantial improvements in CVR prediction accuracy and price consistency. Online A/B testing further validates the effectiveness of our approach.