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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

Alimama Tech

Mar 14, 2025 · Artificial Intelligence

Advances in Search Advertising Models with Large Language Models (2024)

In 2024 Alibaba Mama outlines how large‑language models transform search advertising through a three‑line scaling roadmap—explicit inductive‑bias design, implicit compute growth, and auxiliary CV/NLP advances—implemented via a pre‑train/post‑train/CTR paradigm and the LUM user‑behavior model, promising gains in relevance, recall, and real‑time serving while highlighting inference efficiency challenges.

CTR predictionLarge Language ModelsSearch Advertising

0 likes · 25 min read

Advances in Search Advertising Models with Large Language Models (2024)

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 Modelsdynamic data selection

0 likes · 10 min read

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

DaTaobao Tech

Jan 22, 2025 · Artificial Intelligence

AI Trends 2025: Paths to AGI, Scaling Law Evolution, and Industry Impact

The article surveys the AI revolution driven by foundation models and an evolving Scaling Law, outlining four AGI pathways—large models, intelligent robots, brain‑computer interfaces, and digital life—while highlighting transformer‑based convergence, generative‑first‑principle breakthroughs like DeepSeek‑V3, and transformative industry impacts ranging from consumer robots to Medical 2.0, personalized education, and digital‑simulation platforms such as NVIDIA’s Omniverse.

AGIAIAI Industry

0 likes · 23 min read

AI Trends 2025: Paths to AGI, Scaling Law Evolution, and Industry Impact

DataFunSummit

Nov 20, 2024 · Artificial Intelligence

Integrating Large Language Models into Health E‑commerce Recommendation Systems: Development, Challenges, and Practice

This article reviews the evolution of large‑model recommendation techniques, analyzes the specific challenges of health‑oriented e‑commerce recommendation, and details practical deployments such as LLM‑enhanced cold‑start recall, DeepI2I expansion, and scaling‑law‑driven CTR models within JD Health.

ctre-commercehealth tech

0 likes · 18 min read

Integrating Large Language Models into Health E‑commerce Recommendation Systems: Development, Challenges, and Practice

DataFunTalk

Sep 16, 2024 · Artificial Intelligence

Integrating Large Language Models into Health E‑commerce Recommendation Systems: Development, Challenges, and Practical Deployments

This article reviews the evolution of large‑model recommendation techniques, analyzes the specific demands and obstacles of health‑focused e‑commerce, and details JD Health's practical implementations—including LLM‑enhanced recall, deep item‑to‑item models, and scaling‑law‑driven CTR improvements—while discussing open research questions and future directions.

HealthcareLLM-enhancementLarge Language Models

0 likes · 17 min read

Integrating Large Language Models into Health E‑commerce Recommendation Systems: Development, Challenges, and Practical Deployments

Tencent Advertising Technology

Jul 24, 2024 · Artificial Intelligence

Multi-Embedding Paradigm for Scaling Recommendation Models: Mitigating Embedding Dimensional Collapse

This paper investigates the embedding dimensional collapse problem that hinders scaling of recommendation models and proposes a Multi-Embedding paradigm that learns multiple embeddings per feature with independent expert networks, demonstrating consistent performance gains across major CTR benchmarks and real‑world ad systems.

Artificial IntelligenceCTR predictiondeep learning

0 likes · 10 min read

Multi-Embedding Paradigm for Scaling Recommendation Models: Mitigating Embedding Dimensional Collapse

Tencent Advertising Technology

Jul 19, 2024 · Artificial Intelligence

The Brutal Aesthetics of Data and Compute: Scaling Laws, Generative AI, and the Evolution of Advertising Systems

This article explains how the scaling law—massive data, compute, and a simple transformer architecture—drives generative AI breakthroughs, how Tencent applied this principle to build larger ad models and the "Hunyuan" large model, and how advertising systems must evolve to truly understand content and users.

AIDataModel Management

0 likes · 11 min read

The Brutal Aesthetics of Data and Compute: Scaling Laws, Generative AI, and the Evolution of Advertising Systems

Kuaishou Tech

Jul 17, 2024 · Artificial Intelligence

Key Technical Innovations in Kuaishou’s “Kuaiyi” Large Model and Its Real-World Applications

The article details Kuaishou’s development of the 175B “Kuaiyi” multimodal large model, presenting eight novel technical innovations—from Temporal Scaling Law and MiLe Loss to MoE‑enhanced reward modeling—and describes how these advances enable high‑performance AI services such as the AI Xiao Kuai chatbot across diverse real‑world scenarios.

AI applicationslarge language modelmodel optimization

0 likes · 12 min read

Key Technical Innovations in Kuaishou’s “Kuaiyi” Large Model and Its Real-World Applications

DataFunSummit

Nov 5, 2023 · Artificial Intelligence

Enhancing Recommendation Models with Scaling Law via HCNet and MemoNet: A Memory‑Based Feature‑Combination Approach

This article presents a memory‑driven architecture (HCNet and MemoNet) that equips recommendation models with scaling‑law characteristics by storing and retrieving arbitrary feature‑combination embeddings, evaluates multi‑hash codebooks, memory‑restoring strategies, key‑feature selection, and demonstrates significant offline and online performance gains.

CTR predictionLarge Language ModelsRecommendation systems

0 likes · 15 min read

Enhancing Recommendation Models with Scaling Law via HCNet and MemoNet: A Memory‑Based Feature‑Combination Approach