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Nov 20, 2025 · Artificial Intelligence

How 1688 Reinvented E‑commerce Search with AI‑Powered Generative Retrieval

This article details Alibaba’s 1688 platform’s shift from traditional e‑commerce search to AI‑driven generative retrieval, covering the AI Deep Search 1.0 and 2.0 cascaded frameworks, multimodal capabilities, an end‑to‑end “model‑as‑search‑engine” approach, experimental results, challenges, and future directions.

AIE-commerce SearchGenerative Retrieval

0 likes · 18 min read

How 1688 Reinvented E‑commerce Search with AI‑Powered Generative Retrieval

Meituan Technology Team

Jul 31, 2025 · Artificial Intelligence

8 Must-Read ACL 2025 Papers from Meituan: Generative Retrieval, Multimodal LLMs & More

Meituan’s research team showcases eight ACL 2025 papers spanning generative retrieval, multi‑objective preference alignment, rich‑text image understanding, cross‑language transfer, multimodal math reasoning, and more, offering insights and breakthroughs that can inspire and aid fellow researchers.

ACL 2025Code-SwitchingGenerative Retrieval

0 likes · 15 min read

8 Must-Read ACL 2025 Papers from Meituan: Generative Retrieval, Multimodal LLMs & More

JD Tech Talk

Apr 27, 2025 · Artificial Intelligence

Breaking the Hourglass Phenomenon of Residual Quantization: Enhancing the Upper Bound of Generative Retrieval

This paper investigates the "sandglass" phenomenon in residual‑quantized semantic identifiers for generative search and recommendation, analyzes its causes of path sparsity and long‑tail token distribution, and proposes heuristic and adaptive token‑removal methods that substantially improve model performance in e‑commerce scenarios.

Generative RetrievalRecommendation Systemsadaptive token removal

0 likes · 10 min read

Breaking the Hourglass Phenomenon of Residual Quantization: Enhancing the Upper Bound of Generative Retrieval

JD Cloud Developers

Apr 27, 2025 · Artificial Intelligence

Overcoming the Hourglass Effect in Residual Quantization for Generative Retrieval

This paper investigates the “hourglass” phenomenon in residual‑quantized semantic identifiers for generative search and recommendation, revealing that token concentration in intermediate codebooks causes path sparsity and long‑tail distributions, and proposes heuristic layer removal and adaptive token‑pruning strategies that markedly improve model performance.

Generative RetrievalToken Pruninghourglass phenomenon

0 likes · 13 min read

Overcoming the Hourglass Effect in Residual Quantization for Generative Retrieval

AntTech

Feb 26, 2025 · Artificial Intelligence

Ant Group’s 18 Accepted Papers at AAAI 2025: Summaries and Highlights

This article presents concise English summaries of the 18 Ant Group papers accepted at AAAI 2025, covering topics such as privacy‑preserving large‑model tuning, knowledge‑graph integration, AI‑generated image detection, multi‑task learning, generative retrieval, role‑playing evaluation, and video hallucination mitigation.

AAAI 2025AI EvaluationGenerative Retrieval

0 likes · 29 min read

Ant Group’s 18 Accepted Papers at AAAI 2025: Summaries and Highlights

JD Tech

Dec 14, 2024 · Artificial Intelligence

Generative Retrieval for E‑commerce Search: Lexical and Semantic ID Approaches

This article presents a comprehensive study of generative retrieval for large‑scale e‑commerce search, comparing lexical‑based and Semantic‑ID‑based methods, introducing a Query‑to‑MultiSpan framework, analyzing the sand‑glass distribution problem in residual quantization, and proposing heuristic and adaptive solutions to improve recall and efficiency.

AIE-commerce SearchGenerative Retrieval

0 likes · 20 min read

Generative Retrieval for E‑commerce Search: Lexical and Semantic ID Approaches

DataFunSummit

Dec 12, 2024 · Artificial Intelligence

Exploring Generative Retrieval: Memory Mechanisms, GDR Paradigm, and Practical Applications

This presentation examines generative retrieval (GDR), compares it with sparse and dense retrieval paradigms, analyzes memory‑mechanism challenges from an EACL 2024 paper, reports experimental findings, proposes a hybrid GDR‑dense approach, and outlines real‑world application scenarios and future directions.

GDRGenerative RetrievalMemory Mechanism

0 likes · 13 min read

Exploring Generative Retrieval: Memory Mechanisms, GDR Paradigm, and Practical Applications

JD Retail Technology

Dec 9, 2024 · Artificial Intelligence

Generative Retrieval for E‑commerce Search: Lexical‑Based and Semantic‑ID Approaches

This article presents a comprehensive study of generative retrieval in large‑scale e‑commerce search, detailing lexical‑based and SemanticID‑based methods, their challenges such as long‑tail distribution and token length, experimental evaluations, the discovered "sandglass" effect, and proposed solutions to improve recall and efficiency.

AIE-commerce SearchGenerative Retrieval

0 likes · 20 min read

Generative Retrieval for E‑commerce Search: Lexical‑Based and Semantic‑ID Approaches

DataFunSummit

Nov 28, 2024 · Artificial Intelligence

Generative Retrieval for E‑commerce Search: Lexical and SemanticID Approaches

This article presents a comprehensive study of generative retrieval for large‑scale e‑commerce search, detailing background challenges, the advantages of generative methods, two concrete strategies—Lexical‑based and SemanticID‑based—along with task redesign, preference optimization, constrained beam search, extensive experiments, and future research directions.

E-commerce SearchGenerative Retrievallexical approach

0 likes · 21 min read

Tencent Advertising Technology

Oct 14, 2024 · Artificial Intelligence

Generative Retrieval Based on Yuan Large Model: Implementation and Practice in Tencent Advertising

This paper presents the implementation and practice of generative retrieval based on Yuan large model in Tencent Advertising, addressing three key challenges: user intent capture, model alignment in advertising domain, and high-performance platform design under ROI constraints.

Generative RetrievalHigh‑performance computingModel Optimization

0 likes · 17 min read

Generative Retrieval Based on Yuan Large Model: Implementation and Practice in Tencent Advertising