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Latest from JD Retail Technology

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JD Retail Technology

Mar 25, 2025 · Artificial Intelligence

2024 Advances in Advertising Creative Generation and Selection

In 2024 the advertising team deployed an end‑to‑end AIGC pipeline that automatically creates high‑quality ad images, uses the multimodal Reliable Feedback Network and the million‑size RF1M dataset to filter outputs, builds rich offline and online multimodal representations with contrastive and list‑wise learning, and optimizes ranking architecture to deliver scalable, personalized creative selection.

AIAIGCAdvertising

0 likes · 10 min read

2024 Advances in Advertising Creative Generation and Selection

JD Retail Technology

Mar 18, 2025 · Artificial Intelligence

Multi‑Agent Reinforcement Learning Based Full‑Chain Computation Allocation (MaRCA) for Advertising Systems

MaRCA, a multi‑agent reinforcement‑learning framework, allocates compute across JD’s advertising playback chain by jointly estimating user value, resource consumption, and action outcomes while dynamically adjusting to real‑time load, achieving roughly 15 % higher ad revenue without extra compute resources.

AdvertisingCompute Schedulingdeep learning

0 likes · 18 min read

Multi‑Agent Reinforcement Learning Based Full‑Chain Computation Allocation (MaRCA) for Advertising Systems

JD Retail Technology

Mar 14, 2025 · Artificial Intelligence

CTR-Driven Advertising Image Generation Using Multimodal Large Language Models

The paper presents CAIG, a CTR‑driven advertising image generation pipeline that pre‑trains a multimodal LLM on e‑commerce data, trains a reward model on CTR‑labeled image pairs, and fine‑tunes generation via product‑centric preference optimization, achieving state‑of‑the‑art online and offline performance.

AICTRMultimodal LLM

0 likes · 11 min read

CTR-Driven Advertising Image Generation Using Multimodal Large Language Models

JD Retail Technology

Mar 11, 2025 · Artificial Intelligence

Can Self‑Isolation Streams Detect Anomalies Faster? A Deep Dive into Time‑Series Anomaly Detection

This article presents a comprehensive analysis of a self‑isolation‑based streaming anomaly detection framework, covering business motivations, existing techniques, technical challenges such as pattern anomalies, long‑term memory and concept drift, the core self‑isolation mechanism, memory‑space architecture, experimental evaluations, and practical risk‑control applications.

Anomaly DetectionTime-seriesconcept drift

0 likes · 28 min read

Can Self‑Isolation Streams Detect Anomalies Faster? A Deep Dive into Time‑Series Anomaly Detection

JD Retail Technology

Mar 6, 2025 · Artificial Intelligence

Dynamic Margin Selection for Efficient Deep Learning and Low-Resource Large Model Training

Jia Xing’s research introduces Dynamic Margin Selection, a technique that repeatedly refreshes a core set of boundary‑close samples to train large language models efficiently on limited resources, achieving comparable loss to full‑data training, enabling six‑fold model compression, faster inference, and a proposed exponential scaling law for data‑efficient AI.

ICLRLarge Language ModelsLow-Resource Training

0 likes · 10 min read

Dynamic Margin Selection for Efficient Deep Learning and Low-Resource Large Model Training

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.

AIGPUNPU

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.

AIJD RetailLLM

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 DataPerformance Optimization

0 likes · 18 min read

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