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

• URL: http://arxiv.org/abs/2607.13398v1
• Date: Wed, 15 Jul 2026 02:52:22 GMT
• Title: TMallGS: Scaling Unified Feature and Sequence Modeling for Generative E-commerce Search
• Abstract summary: We propose TmallGS, a scalable ranking architecture for Tmall search.<n>TmallGS includes five key components: (1) Hierarchical Distribution-Calibrated Tokenization; (2) a Field-Adaptive Gated Transformer Backbone; (3) Decoupled FiLM Late Fusion to preserve explicit high-frequency signals; and (4) a Context-Aware Bias Net.<n>Extensive offline experiments and online A/B tests on Tmall Search show that TmallGS improves training throughput and achieves substantial gains in UCTCVR and GMV.
• Score: 4.061689397024572
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In industrial search and ranking systems, Click-Through Rate (CTR) prediction is shifting from traditional Deep Learning Recommendation Models (DLRM) toward unified, compute-intensive Transformer architectures. This transition is driven by the need to improve Model FLOPs Utilization (MFU) and achieve predictable gains through scaling laws. However, existing approaches such as OneTrans and Climber often adopt an all-in-tokenization strategy when adapting Large Language Model (LLM) architectures, overlooking the heterogeneous nature of ranking features. We propose TmallGS, a scalable ranking architecture for Tmall search. TmallGS includes five key components: (1) Hierarchical Distribution-Calibrated Tokenization, which combines Field-wise Saliency Reweighting (FSR) and Distribution-Calibrated Projection (DCP) to map diverse features into optimized subspaces; (2) a Field-Adaptive Gated Transformer Backbone with per-field QKV projections and noise-adaptive gating for refined semantic interaction; (3) Decoupled FiLM Late Fusion to preserve explicit high-frequency signals; (4) a Context-Aware Bias Net to decouple systemic bias from user intent; and (5) Error-Aware Progressive Training with dynamically weighted losses for robust learning. Extensive offline experiments and online A/B tests on Tmall Search show that TmallGS improves training throughput and achieves substantial gains in UCTCVR and GMV.

Related papers

• Unlocking Feature Learning in Gated Delta Networks at Scale [52.705562336559446]
  Training and scaling Large Language Models demand enormous computational resources.<n>We derive the scaling rules for Gated Delta Network.<n> Experiments on language-model pre-training confirm that our configurations enable stable learning-rate transfer.
  arXiv  Detail & Related papers  (2026-06-02T08:45:24Z)
• Customer Churn Prediction on Structured Data Using FT-Transformer and Stacking Ensembles [0.0]
  This study introduces a validated hybrid architecture that integrates feature-tokenized transformers (FT-Transformer) with gradient-boosted trees through calibration stacking.<n>The proposed framework addresses persistent gaps in statistical validation, probability, calibration, and found in prior research.<n>On a public bank churn dataset, the hybrid model 62.10% F1, 0.861 AUC-ROC, and 0.647 PR-AUC, outperforming the Multi-Layer Perceptron (MLP) baseline by 3.37 F1 points and 0.027 AUC with 95% confidence intervals reported.
  arXiv  Detail & Related papers  (2026-05-26T23:02:38Z)
• Position: Weight Space Should Be a First-Class Generative AI Modality [67.10702320971893]
  This paper argues that model checkpoints should be treated as a first-class data modality.<n>It argues that generative modeling in weight space should be standardized as a core machine learning primitive.<n>Our goal is to shift the community's default mindset from optimizing models per task to sampling models from learned weight distributions.
  arXiv  Detail & Related papers  (2026-05-18T16:38:26Z)
• Neural Network Optimization Reimagined: Decoupled Techniques for Scratch and Fine-Tuning [49.751529745537546]
  We propose DualOpt, a novel approach that decouples optimization techniques specifically tailored for training from scratch.<n>For training from scratch, we introduce real-time layer-wise weight decay, designed to enhance both convergence and generalization.<n>We extend the layer-wise weight decay to dynamically adjust the rollback levels across layers, adapting to the varying demands of different downstream tasks.
  arXiv  Detail & Related papers  (2026-04-21T06:27:18Z)
• GFPL: Generative Federated Prototype Learning for Resource-Constrained and Data-Imbalanced Vision Task [43.723840781330914]
  Federated learning (FL) facilitates the secure utilization of decentralized images.<n>FL faces two critical challenges in real-world deployment: ineffective knowledge fusion and prohibitive communication overhead.<n>We propose a novel Generative Federated Prototype Learning framework to address these issues.
  arXiv  Detail & Related papers  (2026-02-25T12:57:45Z)
• EST: Towards Efficient Scaling Laws in Click-Through Rate Prediction via Unified Modeling [13.693397814262681]
  Efficiently scaling industrial Click-Through Rate (CTR) prediction has recently attracted significant research attention.<n>We propose the Efficiently Scalable Transformer (EST), which achieves fully unified modeling by processing all raw inputs in a single sequence without lossy aggregation.<n>EST significantly outperforms production baselines, delivering a 3.27% RPM (Revenue Per Mile) increase and a 1.22% CTR lift.
  arXiv  Detail & Related papers  (2026-02-11T12:51:54Z)
• Continual Action Quality Assessment via Adaptive Manifold-Aligned Graph Regularization [53.82400605816587]
  Action Quality Assessment (AQA) quantifies human actions in videos, supporting applications in sports scoring, rehabilitation, and skill evaluation.<n>A major challenge lies in the non-stationary nature of quality distributions in real-world scenarios.<n>We introduce Continual AQA (CAQA), which equips with Continual Learning capabilities to handle evolving distributions.
  arXiv  Detail & Related papers  (2025-10-08T10:09:47Z)
• DART: Dual Adaptive Refinement Transfer for Open-Vocabulary Multi-Label Recognition [59.203152078315235]
  Open-Vocabulary Multi-Label Recognition (OV-MLR) aims to identify multiple seen and unseen object categories within an image.<n> Vision-Language Pre-training models offer a strong open-vocabulary foundation, but struggle with fine-grained localization under weak supervision.<n>We propose the Dual Adaptive Refinement Transfer (DART) framework to overcome these limitations.
  arXiv  Detail & Related papers  (2025-08-07T17:22:33Z)
• Fourier Test-time Adaptation with Multi-level Consistency for Robust Classification [10.291631977766672]
  We propose a novel approach called Fourier Test-time Adaptation (FTTA) to integrate input and model tuning. FTTA builds a reliable multi-level consistency measurement of paired inputs for achieving self-supervised of prediction. It was extensively validated on three large classification datasets with different modalities and organs.
  arXiv  Detail & Related papers  (2023-06-05T02:29:38Z)
• CSformer: Bridging Convolution and Transformer for Compressive Sensing [65.22377493627687]
  This paper proposes a hybrid framework that integrates the advantages of leveraging detailed spatial information from CNN and the global context provided by transformer for enhanced representation learning. The proposed approach is an end-to-end compressive image sensing method, composed of adaptive sampling and recovery. The experimental results demonstrate the effectiveness of the dedicated transformer-based architecture for compressive sensing.
  arXiv  Detail & Related papers  (2021-12-31T04:37:11Z)
• Improving Transformer-Kernel Ranking Model Using Conformer and Query Term Independence [29.442579683405913]
  The Transformer- Kernel (TK) model has demonstrated strong reranking performance on the TREC Deep Learning benchmark. A variant of the TK model -- called TKL -- has been developed that incorporates local self-attention to efficiently process longer input sequences. In this work, we propose a novel Conformer layer as an alternative approach to scale TK to longer input sequences.
  arXiv  Detail & Related papers  (2021-04-19T15:32:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.