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JD Retail Technology

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Latest from JD Retail Technology

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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.

LLMRecommendation SystemsTensorRT-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.

AILLMMulti-agent

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.

CacheHotKeyPerformance

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

JD Retail Technology

Jan 21, 2025 · Artificial Intelligence

Tech Insight: Selected JD Retail Technology Papers in Artificial Intelligence (2024)

Tech Insight highlights ten 2024 JD Retail Technology AI papers presented at top conferences—including CVPR, SIGIR, WWW, AAAI and IJCAI—that advance open‑vocabulary object detection, unified search‑recommendation, pre‑ranking consistency, diversity‑aware re‑ranking, a diversified product‑search dataset, graph‑based query classification, plug‑in CTR models, parallel ad‑ranking, trajectory‑based CTR stability, and task‑aware decoding for large language models.

Artificial IntelligenceCTR predictionE‑commerce

0 likes · 20 min read

JD Retail Technology

Jan 15, 2025 · Industry Insights

JD Retail’s 2024 Tech Innovations: AI, Supply‑Chain Algorithms, and Development

In 2024 JD Retail Technology delivered a series of breakthroughs—including a major JD APP redesign, a data‑driven inventory selection and allocation algorithm that cut stockouts, an AIGC platform for marketing content, a low‑code national‑subsidy system, a large‑scale Apache Hudi data lake, the Taro‑on‑Harmony cross‑platform framework, immersive XR shopping experiences, and a domestic‑chip AI engine—showcasing how advanced AI, cloud, and operations engineering are reshaping e‑commerce.

AIAIGCCross‑platform development

0 likes · 17 min read

JD Retail’s 2024 Tech Innovations: AI, Supply‑Chain Algorithms, and Development

JD Retail Technology

Jan 10, 2025 · Fundamentals

Clean Code: Principles and Practices for Writing Maintainable Software

The article argues that clean code—characterized by readable naming, minimal comments, short single‑purpose functions, simple control flow, and well‑encapsulated classes—is essential for maintainable software, contrasting it with design patterns and architecture, and demonstrates its benefits through practical refactoring examples and a real‑world case study.

best practicesclean codecode quality

0 likes · 20 min read

Clean Code: Principles and Practices for Writing Maintainable Software

JD Retail Technology

Jan 7, 2025 · Fundamentals

Handling Outliers in Internet A/B Experiments: Concepts, Methods, and Practical Recommendations

The article explains why outliers destabilize internet A/B tests, outlines their causes, compares trimming and winsorizing, presents lightweight detection (e.g., kurtosis) and risk‑control strategies, and offers practical recommendations for bias‑aware removal and variance‑reduction techniques to improve experimental precision.

A/B testingTRIMexperiment design

0 likes · 10 min read

Handling Outliers in Internet A/B Experiments: Concepts, Methods, and Practical Recommendations

JD Retail Technology

Jan 3, 2025 · Backend Development

Improving Software Architecture Efficiency: Stability, Performance, and Code Quality

Improving software architecture efficiency requires stable, orthogonal module design, performance‑focused refactoring that avoids tactical shortcuts, and disciplined layered code that balances business and domain responsibilities, while fostering modularization, decoupling, strict quality standards, and a collaborative culture of continuous improvement.

backend designsoftware architecturesystem stability

0 likes · 12 min read

Improving Software Architecture Efficiency: Stability, Performance, and Code Quality

JD Retail Technology

Dec 27, 2024 · Industry Insights

How JD’s Data‑Driven Inventory Selection Boosted Fulfillment Efficiency

This article details JD Retail's award‑winning, data‑driven inventory selection and allocation algorithms, explains their mathematical models, heuristic and end‑to‑end learning solutions, presents experimental results on real‑world data, and quantifies the operational gains achieved after deployment.

Inventory OptimizationLogisticsSupply Chain

0 likes · 21 min read

How JD’s Data‑Driven Inventory Selection Boosted Fulfillment Efficiency