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JD Tech

Jun 16, 2025 · Artificial Intelligence

How JD Engineers Leverage LLMs and Sparse Models to Boost Search and Ads

This article showcases three JD tech case studies—using large language models for e‑commerce query expansion, applying sparse large models with scaling‑law experiments to improve ad prediction, and building proactive risk‑prevention systems—to illustrate practical AI engineering that drives higher recall, conversion, and system robustness.

advertisinge-commercelarge language model

0 likes · 8 min read

How JD Engineers Leverage LLMs and Sparse Models to Boost Search and Ads

JD Tech Talk

May 22, 2025 · Artificial Intelligence

From Academic Research to Industrial Anti‑Fraud: Leveraging LLMs, Reinforcement Learning, and Model Distillation for Advertising Risk Detection

The article recounts Xiaoting’s journey from a PhD research background to leading JD.com’s ad‑fraud detection, detailing how large language models, reinforcement learning, and model distillation were applied to identify hidden address codes, reduce false‑positive rates to 0.3%, and balance accuracy with real‑time performance in a high‑traffic e‑commerce environment.

AILLMModel Distillation

0 likes · 11 min read

From Academic Research to Industrial Anti‑Fraud: Leveraging LLMs, Reinforcement Learning, and Model Distillation for Advertising Risk Detection

Alimama Tech

May 12, 2025 · Artificial Intelligence

Universal Recommendation Model (URM): A General Large‑Model Recall System for Advertising

The article presents the Universal Recommendation Model (URM), a large‑language‑model‑based recall framework that integrates world knowledge and e‑commerce expertise through knowledge injection and prompt‑driven alignment, achieving significant offline recall gains and a 3.1% increase in ad consumption while meeting high‑QPS, low‑latency production constraints.

advertisinghigh QPSlarge language model

0 likes · 17 min read

Universal Recommendation Model (URM): A General Large‑Model Recall System for Advertising

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.

Large Modelsadvertisingdistributed systems

0 likes · 20 min read

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

JD Tech

Apr 15, 2025 · Artificial Intelligence

Reliable Advertising Creative Generation and Personalized Recommendation via Multimodal Feedback and Offline Representation

The article presents a series of technical breakthroughs by JD's advertising team that improve the quality and coverage of AI‑generated ad images through a trustworthy multimodal feedback network, introduce a large human‑annotated image dataset, and enhance creative ranking with offline multimodal representations and online architecture optimizations, ultimately achieving more precise and scalable ad personalization.

AIAIGCadvertising

0 likes · 10 min read

Reliable Advertising Creative Generation and Personalized Recommendation via Multimodal Feedback and Offline Representation

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.

LLMLarge Language Modelsadvertising

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.

AIAIGCRanking

0 likes · 10 min read

2024 Advances in Advertising Creative Generation and Selection

JD Tech Talk

Mar 19, 2025 · Artificial Intelligence

Reliable Advertising Image Generation and Creative Selection Using Multimodal Feedback and MLLM Representations

The 2024 advertising team introduced a suite of AI‑driven techniques—including a trustworthy feedback network, a large‑scale human‑annotated dataset, multimodal large language model representations, and online ranking architecture upgrades—to dramatically improve the quality, coverage, and personalization of generated ad creatives.

AIGCMLLMadvertising

0 likes · 10 min read

Reliable Advertising Image Generation and Creative Selection Using Multimodal Feedback and MLLM Representations

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

Alimama Tech

Mar 12, 2025 · Big Data

Design and Evolution of Alibaba Advertising Real-Time Data Warehouse

Alibaba Mama’s advertising platform migrated from a monolithic Flink‑Kafka pipeline to a layered Paimon lakehouse, adding DWS upsert support and multi‑layer storage, which delivers minute‑level data freshness, cuts latency by 2.5 hours, reduces resource use over 40 %, halves development effort and achieves ≥99.9 % availability.

AlibabaFlinkPaimon

0 likes · 18 min read

Design and Evolution of Alibaba Advertising Real-Time Data Warehouse

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.

DPOLarge Language Modelsadvertising

0 likes · 12 min read

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

JD Tech Talk

Feb 18, 2025 · Artificial Intelligence

Agent Applications in Advertising at JD.com: Technical Implementation and Platform Architecture

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.

AI PlatformArtificial IntelligenceFunction Call

0 likes · 23 min read

Agent Applications in Advertising at JD.com: Technical Implementation and Platform Architecture

Kuaishou Tech

Dec 17, 2024 · Artificial Intelligence

NeurIPS 2024 Auto‑Bidding in Large‑Scale Auctions: Kuaishou Team Wins Both General and AIGB Tracks

The NeurIPS 2024 Auto‑Bidding competition attracted over 15,000 submissions and 1,500 teams, featuring two tracks—General and AI‑Generated Bidding—where Kuaishou’s commercial algorithm team secured first place in both by leveraging reinforcement‑learning‑based online exploration and a decision‑transformer‑driven generative approach, achieving more than a 5% lift in ad revenue.

Generative ModelsKuaishouNeurIPS

0 likes · 13 min read

NeurIPS 2024 Auto‑Bidding in Large‑Scale Auctions: Kuaishou Team Wins Both General and AIGB Tracks

Ximalaya Technology Team

Nov 29, 2024 · Artificial Intelligence

Applying Large Language Models for AIGC Advertising: Content Generation, Multimodal Understanding, and Creative Optimization at Ximalaya

Ximalaya leverages large language models and AI‑generated content to automate ad creative production, multimodal semantic understanding, and creative selection, slashing image costs to 0.2 CNY, boosting CTR by up to 3.5 %, improving revenue and eCPM by over 2 %, and expanding material diversity fivefold.

AIGCLarge Language ModelsMultimodal Understanding

0 likes · 21 min read

Applying Large Language Models for AIGC Advertising: Content Generation, Multimodal Understanding, and Creative Optimization at Ximalaya

Tencent Advertising Technology

Oct 17, 2024 · Artificial Intelligence

Long Sequence Modeling for Advertising Recommendation: TIN, Disentangled Side‑Info TIN, Stacked TIN, and Target‑aware SASRec

This article presents a comprehensive solution for heterogeneous long‑behavior sequence modeling in advertising recommendation, introducing the TIN backbone, Disentangled Side‑Info TIN, Stacked TIN, and Target‑aware SASRec, along with platform‑level optimizations that enable million‑scale sequences while delivering significant online performance gains.

Transformeradvertisingdeep learning

0 likes · 15 min read

Long Sequence Modeling for Advertising Recommendation: TIN, Disentangled Side‑Info TIN, Stacked TIN, and Target‑aware SASRec

iQIYI Technical Product Team

Oct 10, 2024 · Artificial Intelligence

Online Deep Learning (ODL) for Real‑Time Advertising Effectiveness: Challenges and Solutions

iQIYI’s minute‑level online deep‑learning framework overcomes stability, timeliness, compatibility, delayed feedback, catastrophic forgetting, and i.i.d. constraints through high‑availability pipelines, TensorFlow Example serialization, rapid P2P model distribution, flexible scheduling, disaster‑recovery rollbacks, PU‑loss adjustment, and knowledge‑distillation, delivering a 6.2% revenue boost.

CTR predictionadvertisingdeep learning

0 likes · 9 min read

Online Deep Learning (ODL) for Real‑Time Advertising Effectiveness: Challenges and Solutions

JD Tech Talk

Sep 23, 2024 · Artificial Intelligence

JD Advertising R&D: AI‑Driven Solutions for Traffic Valuation, Multimodal Understanding, Auction Mechanisms, Generative Recommendation, and Large‑Model Engineering

The JD Advertising R&D team applies cutting‑edge AI techniques—including query intent models, multimodal representation pipelines, reinforcement‑learning‑based auction mechanisms, generative recommendation with quantized product tokens, and large‑model infrastructure—to boost traffic valuation, ad relevance, revenue, and creative generation across the platform.

AIGraph Neural NetworksLarge Models

0 likes · 19 min read

JD Advertising R&D: AI‑Driven Solutions for Traffic Valuation, Multimodal Understanding, Auction Mechanisms, Generative Recommendation, and Large‑Model Engineering

iQIYI Technical Product Team

Sep 12, 2024 · Artificial Intelligence

Intelligent Compute Allocation in Advertising: Value Quantification, Elastic Elimination, and Dynamic Optimization

iQIYI’s ad engine team introduced an intelligent compute allocation system that quantifies traffic value and unified compute cost, uses elastic elimination and a dynamic allocation framework to maximize revenue under fixed compute limits, delivering over 30% inventory growth, modest consumption rise, and near‑perfect availability.

Intelligent ComputeOptimizationPID control

0 likes · 11 min read

Intelligent Compute Allocation in Advertising: Value Quantification, Elastic Elimination, and Dynamic Optimization

Alimama Tech

Sep 11, 2024 · Artificial Intelligence

A Generative Approach for Treatment Effect Estimation under Collider Bias: From an Out-of-Distribution Perspective

The paper introduces a coupled generative adversarial framework that merges biased observational with unbiased experimental data to create a bias‑free dataset for causal inference, enabling robust treatment‑effect estimation under collider bias from an out‑of‑distribution perspective, and demonstrates superior bias reduction on three public advertising datasets.

Generative Adversarial Networksadvertisingcausal inference

0 likes · 10 min read

A Generative Approach for Treatment Effect Estimation under Collider Bias: From an Out-of-Distribution Perspective

Tencent Advertising Technology

Aug 27, 2024 · Artificial Intelligence

Auxiliary Ranking Loss Enhances Classification Ability in Sparse‑Feedback CTR Prediction

This study investigates how adding an auxiliary ranking loss to click‑through‑rate (CTR) models not only improves ranking but also alleviates gradient‑vanishing for negative samples, thereby boosting the primary classification performance, especially under sparse positive‑feedback conditions.

CTR predictionadvertisinggradient analysis

0 likes · 13 min read

Auxiliary Ranking Loss Enhances Classification Ability in Sparse‑Feedback CTR Prediction