Integrating recall and pre-ranking in e-commerce search requires candidate generation to account for relevance, personalization, and business value before final ranking. To this end, we present VARG, a generative retrieval system for Tmall App search that directly admits generated item candidates to the existing final ranker. VARG-ID constructs semantic prefixes using RQ-VAE, enhances search relevance through bidirectional query-item contrastive learning, and combines these prefixes with a value-ordered third token to provide fine-grained item addresses and a business-value prior. Three-stage supervised fine-tuning progressively learns item-to-identifier mappings, query-semantic retrieval, and personalized retrieval. Personalized model training combines value-aware and hierarchy-aligned supervision with expanded user context, and uses local ordinal supervision (LO-SFT) to learn the local within-cluster ordering encoded by the third token. Prefix-GRPO combines gated rewards based on output legality, user behavior, ranker advantage, and search relevance with prefix-aware token weighting to align candidate generation with business value and ranking objectives. Coordinated daily product and model updates preserve existing item addresses while incorporating new products and behavioral feedback. Offline experiments on tens of millions of products validate identifier stability and demonstrate gains in retrieval quality and head-level value recall from SFT strategies and Prefix-GRPO over their respective baselines. In a 14-day online A/B test covering 20% of search traffic, VARG directly admits generated candidates to the final ranker and improves GMV by 1.45%, per-user IPV by 0.22%, and PCTR by 0.31%. Online shopping-guide query evaluations further show that VARG maintains competitive relevance with a smaller candidate quota.
Xiao-Peng Chu, Jian-Bo Zhu, Ming-Min Jin et al.· 0 citations
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.
Zhentao Song, Yufeng Gao, Xingye Fang et al.· Proceedings of the 32nd ACM...· 0 citations
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