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JD Retail Technology

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Latest from JD Retail Technology

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JD Retail Technology

Mar 4, 2025 · Artificial Intelligence

JD Retail End-to-End AI Engine Compatible with GPU and Domestic NPU: Architecture, Optimization, and Applications

JD Retail’s Nine‑Number Algorithm Platform delivers an end‑to‑end AI engine that unifies GPU and domestic NPU resources across a thousand‑card cluster, offering zero‑cost model migration, optimized training and inference pipelines, support for over 40 LLM and multimodal models, and proven business‑level performance that reduces dependence on overseas chips.

AIDistributed TrainingGPU

0 likes · 19 min read

JD Retail End-to-End AI Engine Compatible with GPU and Domestic NPU: Architecture, Optimization, and Applications

JD Retail Technology

Mar 1, 2025 · Industry Insights

How JD Retail’s AI Assistant Uses Multimodal LLMs to Boost E‑Commerce

JD Retail’s AI assistant combines a Master‑Sub agent framework, ReAct paradigm, multimodal integration and MoE architecture to improve sales forecasting, pricing, and recommendation accuracy, while the team’s collaborative culture and open talent pathways illustrate how cutting‑edge AI is applied in real‑world e‑commerce.

AIE-commerceJD Retail

0 likes · 8 min read

How JD Retail’s AI Assistant Uses Multimodal LLMs to Boost E‑Commerce

JD Retail Technology

Feb 28, 2025 · Artificial Intelligence

Generative Recommendation with DPO Alignment for JD Alliance Advertising: Multi‑Objective Optimization and Online Results

The paper presents a generative recommendation framework for JD Alliance advertising that combines semantic‑ID modeling, large‑model pre‑training and fine‑tuning, and Direct Preference Optimization (including Softmax‑DPO and β‑DPO) to jointly boost click‑through and conversion rates, achieving +0.6% UCTR and +8% UCVR in online tests while outlining future multi‑objective extensions.

AdvertisingDPOGenerative Recommendation

0 likes · 12 min read

Generative Recommendation with DPO Alignment for JD Alliance Advertising: Multi‑Objective Optimization and Online Results

JD Retail Technology

Feb 25, 2025 · Artificial Intelligence

How JD’s “JingDianDian” AI Platform Revolutionizes E‑commerce Content Creation

JD Retail’s self‑built AIGC platform ‘JingDianDian’ leverages multimodal diffusion models, ControlNet, RAG and reinforcement learning to automatically generate high‑quality product images, videos and marketing copy, cutting production time from days to seconds, slashing costs by over 99% for more than 350 k merchants.

AIGCcontent generationdiffusion models

0 likes · 15 min read

How JD’s “JingDianDian” AI Platform Revolutionizes E‑commerce Content Creation

JD Retail Technology

Feb 20, 2025 · Big Data

Cold‑Hot Data Tiering Solutions for JD Advertising Using Apache Doris

JD Advertising built a petabyte‑scale ad analytics service on Apache Doris, identified a hot‑cold access pattern, and implemented a native cold‑hot tiering solution (upgrading to Doris 2.0 and optimizing schema changes) that cut storage costs by ~87% and boosted concurrent query capacity over tenfold while simplifying operations.

Apache DorisBig DataData Storage

0 likes · 18 min read

Cold‑Hot Data Tiering Solutions for JD Advertising Using Apache Doris

JD Retail Technology

Feb 18, 2025 · Artificial Intelligence

Engineering Practices of JD Advertising Agent: JDZunTong Intelligent Assistant

JD’s advertising R&D team created the JDZunTong Intelligent Assistant by engineering a modular Agent platform that combines advanced Retrieval‑Augmented Generation (RAG 1.0 → 2.0) and Function‑Call capabilities, a visual designer, custom tool registration, and a native Python workflow engine to deliver intelligent customer service, data queries, and ad creation for merchants.

AIAgentFunction Call

0 likes · 18 min read

Engineering Practices of JD Advertising Agent: JDZunTong Intelligent Assistant

JD Retail Technology

Feb 12, 2025 · Artificial Intelligence

Accelerating Generative Recommendation with NVIDIA TensorRT‑LLM in JD Advertising

JD Advertising accelerates its generative‑recall recommendation system by integrating NVIDIA TensorRT‑LLM, which simplifies the pipeline, injects LLM knowledge, scales to billions of parameters, and delivers over five‑fold throughput gains, one‑fifth the cost, and significant CTR improvements in both recommendation and search.

Inference OptimizationLLMTensorRT-LLM

0 likes · 13 min read

Accelerating Generative Recommendation with NVIDIA TensorRT‑LLM in JD Advertising

JD Retail Technology

Feb 10, 2025 · Artificial Intelligence

JD Merchant Intelligent Assistant: Multi‑Agent Architecture and Technical Exploration

The JD Merchant Intelligent Assistant employs a large‑language‑model‑driven multi‑agent architecture with dynamic ReAct planning, enabling merchants to query and execute store operations in under a second with over 90 % decision accuracy, while reducing inference cost, hallucinations, and engineering effort across diverse e‑commerce tasks.

AIEvaluationLLM

0 likes · 25 min read

JD Merchant Intelligent Assistant: Multi‑Agent Architecture and Technical Exploration

JD Retail Technology

Feb 7, 2025 · Backend Development

Cache Big‑Key and Hot‑Key Issues: Case Study, Root‑Cause Analysis, and Mitigation Strategies

A promotional event created an oversized Redis cache entry that, combined with cache‑penetration bursts, saturated network bandwidth and caused a service outage, prompting mitigation through Protostuff serialization, gzip compression, request throttling, and enhanced monitoring, while recommending design‑time cache planning and stress testing to prevent future big‑key failures.

BackendBigKeyCache

0 likes · 9 min read

Cache Big‑Key and Hot‑Key Issues: Case Study, Root‑Cause Analysis, and Mitigation Strategies

JD Retail Technology

Jan 24, 2025 · Databases

How to Seamlessly Migrate a Legacy MongoDB System to New Storage

This article presents a complete, step‑by‑step migration plan for a legacy MongoDB‑based system, covering scope analysis, data‑store selection, DAO refactoring, dual‑write synchronization, bulk and incremental data migration, and the three‑pronged deployment strategy of monitoring, gray‑release and rollback to ensure a smooth cut‑over without service disruption.

ArchitectureDatabase MigrationMongoDB

0 likes · 7 min read

How to Seamlessly Migrate a Legacy MongoDB System to New Storage