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This tutorial explains how to use Python to scrape product reviews from JD.com via its AJAX comment API, extract fields such as nickname, score, content, and image count, and save the collected data into a CSV file using the requests and csv modules.
The article presents a comprehensive adaptive degradation and automatic recovery framework for JD Alliance's recommendation system, designed to handle high‑frequency, instantaneous traffic surges during large promotions by combining real‑time monitoring, Wilson‑interval‑based timeout correction, scenario‑aware control, traffic slicing, linear‑programming‑driven chain optimization, and low‑cost business‑agnostic APIs, achieving over 90% reduction in traffic loss and zero incidents.
This article explores how JD.com's advertising team leverages Agent technology to enhance advertising operations through AI-driven automation, covering technical implementations like RAG, Function Call capabilities, and platform architecture for scalable AI solutions.
This article details how JD's BIP procurement system tackled massive query‑performance challenges by segmenting order data, leveraging the JED distributed MySQL solution, introducing JimKV for hot‑data caching, and offloading complex searches to Elasticsearch, resulting in dramatically reduced load and faster user experiences.
JD.com’s data‑governance framework combines a health‑score‑driven, automated platform that cross‑verifies audit logs, builds full‑link and operator‑level lineage, introduces standard fields, and optimizes resource mixing, task staggering, and cross‑datacenter scheduling, while targeting real‑time AI‑enhanced detection and full automation.
This article presents JD.com’s comprehensive big‑data governance experience, detailing the background and challenges, the automated governance platform and its core technologies such as audit logs and full‑link lineage, strategies for resource optimization, and the roadmap toward real‑time, intelligent, and fully automated data governance.
This article presents JD.com’s multimodal recommendation system architecture, covering content understanding, multimodal ranking and recall models, practical deployment pipelines, and future research directions such as large‑model integration and supply‑side generation, all illustrated with detailed diagrams and Q&A.
In this interview, JD Retail’s AI director Zhai Zhouwei outlines the four historical stages of e‑commerce search, explains how large‑model AI is reshaping user interaction, retrieval and content generation, discusses practical challenges and solutions, and shares his vision and advice for enterprises adopting these technologies.
This article describes how JD Alliance built an adaptive degradation and automatic recovery framework for its recommendation system to handle sudden, large‑scale traffic spikes during major sales events, ensuring stability while minimizing recommendation loss through real‑time monitoring, scenario‑aware control, and linear‑programming‑based pipeline orchestration.
The JD Group's Project Management Talent Development Channel presented a series of expert talks and a round‑table at the third China PMO & PM Conference, covering data‑driven project intelligence, best practices in internet project management, AI‑enhanced workflows, and overseas delivery challenges, while highlighting JD's commitment to cultivating project management excellence.
On August 10‑11, JD Group's Project Management Talent Development Channel showcased its expertise at the 3rd China PMO&PM Conference in Shanghai, delivering multiple expert talks on data‑driven project management, R&D efficiency, AI‑enabled workflows, overseas delivery, and fostering industry dialogue through round‑table discussions.
This article outlines the JD Insurance UED team's recent explorations and achievements in leveraging AIGC technology to transform creative design workflows, highlighting key insights, visual examples, and recommended readings to inspire broader adoption of AI‑generated content tools across the industry.
This article presents an in‑depth overview of JD.com’s retail data visualization platform, detailing its product matrix—including EasyBI, a low‑code platform, and JDV large‑screen tool—its architectural layers, key capabilities, business case studies, challenges faced, and future development directions.
The article presents a comprehensive overview of JD's advertising retrieval platform, detailing how it balances limited compute resources with massive data through adaptive compute allocation, distributed execution graphs, elastic systems, and multi‑stage algorithmic improvements to achieve high‑performance, scalable ad matching.
This article chronicles the author's three‑year journey as an AI product manager at JD.com, detailing the decision to enter the AI field, key project experiences such as platform integration and edge‑intelligence, a three‑stage growth roadmap, essential capabilities across four quadrants, and a forward‑looking view of AI's industry impact.
This article presents an in‑depth overview of JD's retail data visualization platform, detailing its product matrix (EasyBI, low‑code platform, JDV), real‑world business use cases, architectural challenges, future development strategies, and a Q&A session that highlights technical and operational insights.
This technical presentation describes JD's data service platform, covering its origin, performance optimizations, flexible API generation, scaling to massive metrics, caching strategies, service orchestration, governance, and a Q&A on security and data‑source flexibility.
The article recounts JD’s journey from a small team building an in‑house advertising system that saved billions, through successive PC, mobile, and middle‑platform eras, to current AI‑driven retail innovations, highlighting strategic decisions, architectural challenges, and the importance of aligning technology with business goals.
This article presents JD Retail's digital‑twin framework for its complex supply‑chain network, detailing the underlying AI‑driven intelligent diagnosis, end‑to‑end simulation platform, two concrete case studies on inventory sinking and multi‑warehouse layout optimization, and outlines future research challenges and opportunities.
This whitepaper outlines JD.com's practical zero‑trust security approach, detailing a five‑element framework that includes asset digitization, asset identity, diversified security checkpoints, a strategy center, and a zero‑trust cockpit, to help digital enterprises strengthen security, reduce costs, and meet regulatory requirements.