Cover Image

MTGR: Industrial-Scale Generative Recommendation Framework in Meituan

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Han, Ruidong, Yin, Bin, Chen, Shangyu, Jiang, He, Jiang, Fei, Li, Xiang, Ma, Chi, Huang, Mincong, Li, Xiaoguang, Jing, Chunzhen, Han, Yueming, Zhou, Menglei, Yu, Lei, Liu, Chuan, Lin, Wei
Format:	Preprint
Published:	2025
Subjects:	Information Retrieval
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

MTFM: A Scalable and Alignment-free Foundation Model for Industrial Recommendation in Meituan
by: Song, Xin, et al.
Published: (2026)

MTGenRec: An Efficient Distributed Training System for Generative Recommendation Models in Meituan
by: Wang, Yuxiang, et al.
Published: (2025)

Context-based Fast Recommendation Strategy for Long User Behavior Sequence in Meituan Waimai
by: Feng, Zhichao, et al.
Published: (2024)

Enhancing CTR Prediction through Sequential Recommendation Pre-training: Introducing the SRP4CTR Framework
by: Han, Ruidong, et al.
Published: (2024)

MBGR: Multi-Business Prediction for Generative Recommendation at Meituan
by: Li, Changhao, et al.
Published: (2026)

DOS: Dual-Flow Orthogonal Semantic IDs for Recommendation in Meituan
by: Yin, Junwei, et al.
Published: (2026)

Adaptive Utilization of Cross-scenario Information for Multi-scenario Recommendation
by: Shu, Xiufeng, et al.
Published: (2024)

Next-Scale Generative Reranking: A Tree-based Generative Rerank Method at Meituan
by: Wang, Shuli, et al.
Published: (2026)

Aligning Explanations for Recommendation with Rating and Feature via Maximizing Mutual Information
by: Zhao, Yurou, et al.
Published: (2024)

Large Language Model as Universal Retriever in Industrial-Scale Recommender System
by: Jiang, Junguang, et al.
Published: (2025)

A Soft-partitioned Semi-supervised Collaborative Transfer Learning Approach for Multi-Domain Recommendation
by: Liu, Xiaoyu, et al.
Published: (2025)

Multimodal Generative Retrieval Model with Staged Pretraining for Food Delivery on Meituan
by: Chen, Boyu, et al.
Published: (2026)

LARR: Large Language Model Aided Real-time Scene Recommendation with Semantic Understanding
by: Wan, Zhizhong, et al.
Published: (2024)

Understanding the Ranking Loss for Recommendation with Sparse User Feedback
by: Lin, Zhutian, et al.
Published: (2024)

RankMixer: Scaling Up Ranking Models in Industrial Recommenders
by: Zhu, Jie, et al.
Published: (2025)

LONGER: Scaling Up Long Sequence Modeling in Industrial Recommenders
by: Chai, Zheng, et al.
Published: (2025)

Unified Dual-Intent Translation for Joint Modeling of Search and Recommendation
by: Zhang, Yuting, et al.
Published: (2024)

RankUp: Towards High-rank Representations for Large Scale Advertising Recommender Systems
by: Chen, Jin, et al.
Published: (2026)

You Only Evaluate Once: A Tree-based Rerank Method at Meituan
by: Wang, Shuli, et al.
Published: (2025)

TokenMixer-Large: Scaling Up Large Ranking Models in Industrial Recommenders
by: Jiang, Yuchen, et al.
Published: (2026)

An Industrial-Scale Sequential Recommender for LinkedIn Feed Ranking
by: Hertel, Lars, et al.
Published: (2026)

Reinforced Preference Optimization for Recommendation
by: Tan, Junfei, et al.
Published: (2025)

RecFlow: An Industrial Full Flow Recommendation Dataset
by: Liu, Qi, et al.
Published: (2024)

Think before Recommendation: Autonomous Reasoning-enhanced Recommender
by: Kong, Xiaoyu, et al.
Published: (2025)

LEARN: Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application
by: Jia, Jian, et al.
Published: (2024)

STARS: Semantic Tokens with Augmented Representations for Recommendation at Scale
by: Chen, Han, et al.
Published: (2025)

Unified Value Alignment for Generative Recommendation in Industrial Advertising
by: Zhang, Xinxun, et al.
Published: (2026)

MSN: A Memory-based Sparse Activation Scaling Framework for Large-scale Industrial Recommendation
by: Wu, Shikang, et al.
Published: (2026)

TokenFormer: Unify the Multi-Field and Sequential Recommendation Worlds
by: Zhou, Yifeng, et al.
Published: (2026)

OneRanker: Unified Generation and Ranking with One Model in Industrial Advertising Recommendation
by: Sun, Dekai, et al.
Published: (2026)

IterQR: An Iterative Framework for LLM-based Query Rewrite in e-Commercial Search System
by: Chen, Shangyu, et al.
Published: (2025)

FedCRF: A Federated Cross-domain Recommendation Method with Semantic-driven Deep Knowledge Fusion
by: Guo, Lei, et al.
Published: (2026)

From Transfer to Collaboration: A Federated Framework for Cross-Market Sequential Recommendation
by: Chen, Jundong, et al.
Published: (2026)

DAS: Dual-Aligned Semantic IDs Empowered Industrial Recommender System
by: Ye, Wencai, et al.
Published: (2025)

A Model-based Multi-Agent Personalized Short-Video Recommender System
by: Zhou, Peilun, et al.
Published: (2024)

R4ec: A Reasoning, Reflection, and Refinement Framework for Recommendation Systems
by: Gu, Hao, et al.
Published: (2025)

Bending the Scaling Law Curve in Large-Scale Recommendation Systems
by: Ding, Qin, et al.
Published: (2026)

MCRPL: A Pretrain, Prompt & Fine-tune Paradigm for Non-overlapping Many-to-one Cross-domain Recommendation
by: Liu, Hao, et al.
Published: (2024)

MixFormer: Co-Scaling Up Dense and Sequence in Industrial Recommenders
by: Huang, Xu, et al.
Published: (2026)

ReST: A Plug-and-Play Spatially-Constrained Representation Enhancement Framework for Local-Life Recommendation
by: Jiang, Hao, et al.
Published: (2025)