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Latest from JD Retail Technology

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JD Retail Technology

Apr 22, 2025 · Artificial Intelligence

Generative Large‑Model Architecture for JD Advertising: Practices, Challenges, and Optimization

JD’s advertising platform replaces rule‑based recall with a generative large‑model pipeline that unifies e‑commerce knowledge, multimodal user intent, and semantic IDs across recall, coarse‑ranking, fine‑ranking and creative optimization, while meeting sub‑100 ms latency and sub‑¥1‑per‑million‑token cost through quantization, parallelism, caching, and joint generative‑discriminative inference, delivering double‑digit performance gains and paving the way for domain‑specific foundation models.

AdvertisingDistributed SystemsInference Optimization

0 likes · 20 min read

Generative Large‑Model Architecture for JD Advertising: Practices, Challenges, and Optimization

JD Retail Technology

Apr 16, 2025 · Artificial Intelligence

AI‑Driven 3D Spatial Video Generation from Monocular 2D Content with MV‑HEVC Encoding

This work presents an end‑to‑end AI pipeline that transforms existing monocular 2D videos into immersive 3D spatial streams by combining DINO‑v2‑based depth estimation, multi‑branch view synthesis, and MV‑HEVC encoding, achieving up to 33 % BD‑Rate reduction, 31 % speed gains, state‑of‑the‑art visual quality, and real‑time production suitability, validated on the new StereoV1K benchmark and deployed in JD.Vision’s e‑commerce catalog.

3D videoAI generationAIGC

0 likes · 21 min read

AI‑Driven 3D Spatial Video Generation from Monocular 2D Content with MV‑HEVC Encoding

JD Retail Technology

Apr 10, 2025 · Artificial Intelligence

How JD’s TimeHF Billion‑Scale Time‑Series Model Boosts Forecast Accuracy with RLHF

JD’s supply‑chain algorithm team introduces TimeHF, a billion‑scale time‑series large model that leverages RLHF to improve demand forecasting accuracy by over 10%, detailing dataset creation, the PCTLM architecture, a custom RL framework (TPO), and superior benchmark results.

AIPCTLMRLHF

0 likes · 12 min read

How JD’s TimeHF Billion‑Scale Time‑Series Model Boosts Forecast Accuracy with RLHF

JD Retail Technology

Apr 8, 2025 · Databases

ClickHouse Architecture and Core Technologies Overview

ClickHouse is an open‑source, massively parallel, column‑oriented OLAP database that integrates its own columnar storage, vectorized batch processing, pre‑sorted data, diverse table engines, extensive data types, sharding with replication, sparse primary‑key and skip indexes, and a multithreaded query engine, delivering high‑throughput real‑time analytics on massive datasets.

Big DataClickHouseDistributed Architecture

0 likes · 15 min read

ClickHouse Architecture and Core Technologies Overview

JD Retail Technology

Apr 2, 2025 · Artificial Intelligence

One4All: A Scalable Multi‑Task Generative Recommendation Framework for CPS Advertising

The paper introduces One4All, a scalable multi‑task generative recommendation framework for CPS advertising that combines few‑shot intent prompting, a Rewards‑in‑Context multi‑objective optimization, and an online model‑selection strategy, delivering 2‑3× offline HitRate/NDCG gains and notable online CTR, CVR, and commission improvements.

AdvertisingLLMlarge language models

0 likes · 14 min read

One4All: A Scalable Multi‑Task Generative Recommendation Framework for CPS Advertising

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.

AICTRE-commerce

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 detectionconcept driftmemory space

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.

ICLRLow-Resource TrainingModel Compression

0 likes · 10 min read

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