TMallGS: Scaling Unified Feature and Sequence Modeling for Generative E-commerce Search

Abstract

In large-scale industrial search and ranking systems, Click-Through Rate (CTR) prediction is undergoing a paradigm shift from traditional Deep Learning Recommendation Models (DLRM) toward unified, compute-intensive Transformer architectures. The primary motivation for this transition is to leverage Model FLOPs Utilization (MFU) to achieve predictable performance gains through Scaling Laws. However, existing scaling approaches like OneTrans and Climber, often adopt an all-in-tokenization strategy when directly migrating Large Language Model (LLM) architectures, which neglects the unique feature heterogeneity. We propose TmallGS, a high-performance, scalable universal ranking architecture tailored for the Tmall precision ranking domain. TmallGS introduces five core innovations: (1) Hierarchical Distribution-Calibrated Tokenization: To bridge the heterogeneity gap, we propose a coarse-to-fine pipeline combining Field-wise Saliency Reweighting (FSR) and Distribution-Calibrated Projection (DCP) to project diverse features into optimized subspaces. (2) Field-Adaptive Gated Transformer Backbone: We employ Per-Field QKV projections and a noise-adaptive gating mechanism to refine semantic interactions and suppress element-wise noise. (3) Decoupled FiLM Late Fusion: To preserve high-frequency explicit signals, we utilize Feature-wise Linear Modulation (FiLM) to dynamically modulate backbone embeddings with explicit cross-features. (4) Context-Aware Bias Decoupling: Addressing systemic biases beyond position, we incorporate a Context-Aware Bias Net that leverages deep global context to orthogonally decouple bias factors from genuine user intent. (5) Error-Aware Progressive Training: We propose a dynamically weighted loss function based on hierarchical prediction errors, which enables adaptive hard-sample mining to improve model robustness. Extensive offline experiments and online A/B tests conducted in the Tmall. Tmall is China's largest B2C e-commerce platform. Search Ranking stage demonstrate that TmallGS significantly boosts training throughput while delivering substantial gains in both UCTCVR and GMV metrics.