Tagged articles

recommendation systems

502 articles · Page 1 of 6

Machine Learning Algorithms & Natural Language Processing

Sep 14, 2026 · Artificial Intelligence

Astar: Alibaba & Zhejiang Univ's AI That Guides AI Evolution, Beating Human Experts 100x Faster

Alibaba and Zhejiang University's Astar learns from AI systems' own Git history to propose evolution strategies, achieving 54–68% single-shot success rates versus 31% for GPT-5.5 and 32% for human experts, and delivering 23.6% offline HitRate and 4.86% GMV gains in Lazada's ad system through 20 fully automated iterations.

AI evolutionAlibabaAstar

0 likes · 15 min read

Astar: Alibaba & Zhejiang Univ's AI That Guides AI Evolution, Beating Human Experts 100x Faster

Machine Heart

Sep 13, 2026 · Artificial Intelligence

Astar: Alibaba's LLM Guides AI System Evolution by Learning from Historical Commits

Alibaba and Zhejiang University introduce Astar, an LLM that learns from AI systems' historical code commits and experiment results to autonomously propose evolution directions, achieving 54-68% single-generation success rates, outperforming GPT-5.5 and human experts, and delivering 23.6% offline HitRatio and 4.86% online GMV gains on Lazada's recommendation system.

AI system evolutionAlibabaAstar

0 likes · 16 min read

Astar: Alibaba's LLM Guides AI System Evolution by Learning from Historical Commits

Data Party THU

Sep 9, 2026 · Artificial Intelligence

Online and Off-Policy Learning for Large Action Spaces: Structured Exploration & Optimization

This PhD thesis addresses large action space contextual bandits, proposing mixed-effects and diffusion Thompson sampling for online learning, and structured direct methods, policy-weighted likelihood, exponential smoothing, and PAC-Bayes pessimism for offline learning, showing that action structure, optimizable objectives, and pessimism are crucial for scalable decision-making.

PAC-BayesThompson samplingcontextual bandits

0 likes · 21 min read

Online and Off-Policy Learning for Large Action Spaces: Structured Exploration & Optimization

Fei's Miscellaneous Talks

Sep 6, 2026 · Artificial Intelligence

How Prediction Servers Score 1000 Candidates in Milliseconds: Fine-Ranking Architecture Deep Dive

This article details the architecture and optimization of a Prediction Server for fine-ranking in recommender systems, covering model management, batch inference, multi-objective fusion, probability calibration, deployment pipelines, and high-availability patterns to score thousands of candidates within milliseconds.

ONNX Runtimebatch inferencefine ranking

0 likes · 46 min read

How Prediction Servers Score 1000 Candidates in Milliseconds: Fine-Ranking Architecture Deep Dive

Tencent Advertising Technology

Sep 4, 2026 · Artificial Intelligence

Scaling Model Width Alone Hurts AUC: Two Seniors' 1000-Experiment TAAC 2026 Breakthrough

Two senior undergraduates won the Scaling Law Original Breakthrough Award and 10th place in the academic track at the 2026 Tencent Advertising Algorithm Competition by running nearly 1000 experiments, discovering that increasing model width alone reduces AUC unless accompanied by new informative tokens, leading to their CoSFormer architecture that scales capacity and information jointly.

AUCCoSFormerTAAC 2026

0 likes · 8 min read

Scaling Model Width Alone Hurts AUC: Two Seniors' 1000-Experiment TAAC 2026 Breakthrough

Alibaba International Intelligent Technology

Sep 3, 2026 · Artificial Intelligence

Masked Diffusion Challenges Left-to-Right Decoding in Generative Recommendation (Recsys'26)

This paper introduces MDGR, a masked diffusion framework for generative recommendation that replaces autoregressive left-to-right decoding with a parallel mask-denoising process, achieving up to 6.56% relative improvement offline and significant online gains in Alibaba's advertising platform.

AlibabaMasked DiffusionOnline A/B Testing

0 likes · 17 min read

Masked Diffusion Challenges Left-to-Right Decoding in Generative Recommendation (Recsys'26)

Tencent Advertising Technology

Aug 19, 2026 · Artificial Intelligence

How I Won the KDD Cup Using DeepSeek’s Web Interface

The author details how, without any API access, they leveraged DeepSeek’s web interface to iteratively develop and refine the QueryFormer model—solving multi‑GPU batch issues, enhancing query generation, and ultimately achieving the TAAC × KDD Cup 2026 industrial track championship.

AutoMLDeepSeekKDD Cup

0 likes · 27 min read

How I Won the KDD Cup Using DeepSeek’s Web Interface

DataFunSummit

Aug 18, 2026 · Artificial Intelligence

How Agentic Architectures Power Next‑Gen Recommendation and Search Systems

The article analyzes cutting‑edge agentic RAG designs, LLM‑enhanced recommendation pipelines, and generative ranking models from Alibaba Cloud, Huawei Noah, and Baidu, detailing their architectures, multi‑modal retrieval strategies, GPU acceleration, and measured performance gains.

Agentic RAGAlibaba CloudBaidu

0 likes · 6 min read

How Agentic Architectures Power Next‑Gen Recommendation and Search Systems

DataFunSummit

Aug 16, 2026 · Artificial Intelligence

How Multi‑Agent Architectures Power the Next Generation of Recommendation and Search Systems

The article reviews cutting‑edge AI search and recommendation techniques—including Agentic RAG, multi‑modal retrieval, GPU‑accelerated indexing, and Baidu’s generative ranking model GRAB—detailing their architectures, optimization strategies, and measured performance gains such as a 1.5% AUC lift.

AI SearchAgentic RAGGPU acceleration

0 likes · 6 min read

How Multi‑Agent Architectures Power the Next Generation of Recommendation and Search Systems

Tencent Advertising Technology

Aug 13, 2026 · Artificial Intelligence

Real-World Industry Problems Take the KDD Stage: Full Highlights of the TAAC 2026 Jeju Island Workshop

The TAAC × KDD Cup 2026 workshop in Jeju Island gathered researchers and engineers from academia and leading tech firms to dissect real‑world recommendation challenges, present the competition’s unified sequence‑modeling and feature‑interaction task, and showcase winning solutions that balance effectiveness, efficiency, and scalability.

KDD CupTAAC 2026large-scale AI

0 likes · 7 min read

Real-World Industry Problems Take the KDD Stage: Full Highlights of the TAAC 2026 Jeju Island Workshop

DataFunSummit

Aug 13, 2026 · Artificial Intelligence

Agent Architecture in Action: Building Next‑Gen Recommendation & Search Systems

The article reviews a collection of technical chapters that analyze how multi‑agent AI architectures, large‑language‑model enhancements, and generative ranking models are applied to solve high‑concurrency, multimodal, and multi‑hop challenges in modern recommendation and search systems, presenting concrete designs, performance numbers, and real‑world case studies.

AI SearchAgentic RAGGenerative Ranking

0 likes · 6 min read

Agent Architecture in Action: Building Next‑Gen Recommendation & Search Systems

Meituan Technology Team

Aug 13, 2026 · Artificial Intelligence

Highlights of Meituan’s KDD’26 Papers and the Champion Strategies for the DataAgents Track

This article presents eight Meituan research papers accepted at KDD 2026—covering recommendation foundation models, contrast‑driven reward modeling, agentic search benchmarks, deterministic ad allocation, cross‑domain ETA meta‑generalization, generative ad‑bidding, hierarchical multi‑slot allocation, and distributed generative recommendation training—along with a detailed walkthrough of the team’s winning approach in the KDD Cup 2026 DataAgents competition.

Advertising OptimizationData AgentsKDD 2026

0 likes · 14 min read

Highlights of Meituan’s KDD’26 Papers and the Champion Strategies for the DataAgents Track

iQIYI Technical Product Team

Aug 6, 2026 · Artificial Intelligence

Scaling Up iQIYI’s Ad CVR Model: 10× Parameter Growth and GPU Inference Deployment

The article details how iQIYI migrated its ad conversion‑rate (CVR) model from a TensorFlow‑CPU pipeline to a TorchRec‑based PyTorch‑GPU architecture, expanding parameters tenfold while aligning offline AUC and online performance, and outlines the systematic optimizations that yielded both business metric gains and cost reductions.

CVR modelGPU inferencePerformance Optimization

0 likes · 22 min read

Scaling Up iQIYI’s Ad CVR Model: 10× Parameter Growth and GPU Inference Deployment

Kuaishou Tech

Aug 4, 2026 · Artificial Intelligence

KDD 2026 Highlights: 25 Kuaishou Papers Selected, 3 Oral Presentations

The Kuaishou technology team had 25 papers accepted at the prestigious KDD 2026 conference—including three oral presentations—covering generative recommendation, automated bidding, semantic ID learning, multi‑behavior modeling, and other AI‑driven advances that are already deployed at massive scale on the platform.

AdvertisingGenerative ModelsKDD2026

0 likes · 33 min read

KDD 2026 Highlights: 25 Kuaishou Papers Selected, 3 Oral Presentations

Airbnb Technology Team

Aug 4, 2026 · Artificial Intelligence

How Airbnb Uses a Transformer Sequence Model to Personalize Search by Learning Guest Journeys

Airbnb built a Transformer‑based sequence model that encodes years of guest behavior—including long‑term bookings and short‑term browsing—to deliver timely, personalized search results, achieving up to 3.78% overall ranking improvement and significant gains in bookings and clicks.

AirbnbSearch PersonalizationSequence Modeling

0 likes · 12 min read

How Airbnb Uses a Transformer Sequence Model to Personalize Search by Learning Guest Journeys

DataFunSummit

Jul 28, 2026 · Artificial Intelligence

Designing Next‑Gen Recommendation and Search with Multi‑Agent AI Architecture

The article reviews a series of technical case studies—including Alibaba Cloud AI Search's Agentic RAG, Baidu's GRAB generative ranking, Huawei Noah's LLM‑enhanced recommendation, and Elasticsearch vector RAG—showing how multi‑agent AI architectures address high‑concurrency, multimodal, and multi‑hop query challenges while delivering measurable performance gains.

AI agentsAlibaba Cloud AI SearchBaidu GRAB

0 likes · 6 min read

Designing Next‑Gen Recommendation and Search with Multi‑Agent AI Architecture

DataFunSummit

Jul 23, 2026 · Artificial Intelligence

How Agentic Architectures Power Next‑Gen Recommendation and Search Systems

The article reviews cutting‑edge AI search and recommendation techniques—including Alibaba Cloud's Agentic RAG, Huawei Noah's LLM‑enhanced recommendation evolution, and Baidu's generative ranking model GRAB—detailing their architectures, multi‑modal retrieval strategies, performance gains, and real‑world deployment insights.

AI SearchAgentic RAGAlibaba Cloud

0 likes · 6 min read

DataFunSummit

Jul 22, 2026 · Artificial Intelligence

Designing Next‑Generation Recommendation and Search Systems with Agentic Architectures

The article analyzes how agentic architectures, large language models, and generative ranking techniques are applied to overcome high‑concurrency, multimodal, and multi‑hop challenges in modern recommendation and search systems, showcasing concrete designs, performance gains, and real‑world deployments from Alibaba Cloud, Huawei Noah, and Baidu.

AI SearchAgentic RAGGenerative Ranking

0 likes · 5 min read

Designing Next‑Generation Recommendation and Search Systems with Agentic Architectures

Tencent Advertising Technology

Jul 14, 2026 · Industry Insights

In‑Depth Interview: HKU Vice President and Tencent Advertising Tech Lead Discuss the Future of AI Talent

A comprehensive dialogue between Hong Kong Chinese University’s vice president and Tencent’s advertising AI experts explores AI frontiers, trustworthy and embodied AI, the four‑generation evolution of recommendation systems, the role of the Tencent Advertising Algorithm Competition in talent development, and practical advice for young professionals navigating the AI‑driven advertising industry.

AIAdvertisingAgentic AI

0 likes · 21 min read

In‑Depth Interview: HKU Vice President and Tencent Advertising Tech Lead Discuss the Future of AI Talent

Machine Heart

Jul 12, 2026 · Artificial Intelligence

Agentic Era: Shifting Recommendation from Platform-Centric to User-Governed

Recent research argues that the traditional platform‑centric recommendation paradigm is reaching its limits, proposing a user‑governed personalization model enabled by LLM agents that can aggregate cross‑platform data, with experimental evidence showing significant performance gains over platform‑only approaches.

Artificial IntelligenceLLM agentscross-platform data

0 likes · 20 min read

Agentic Era: Shifting Recommendation from Platform-Centric to User-Governed

DataFunSummit

Jul 11, 2026 · Artificial Intelligence

Agent Architecture and Practice: Building the Next‑Generation Recommendation and Search Systems

The article analyzes the technical evolution of AI‑driven recommendation and search, covering Alibaba Cloud's Agentic RAG architecture, Huawei Noah's LLM‑enhanced recommendation pipeline, and Baidu's generative ranking model GRAB, while presenting design choices, performance metrics, and real‑world deployment results.

AI agentsAgentic RAGGenerative Ranking

0 likes · 5 min read

Agent Architecture and Practice: Building the Next‑Generation Recommendation and Search Systems

vivo Internet Technology

Jul 8, 2026 · Artificial Intelligence

How AI Shifts Recommendation Systems from Simply Pushing Items to Guiding User Choices

The article examines how large‑language models can augment a game‑distribution recommender by keeping accurate ranking while adding an expression and decision layer that explains differences between similar titles, using a structured schema, an exploration‑to‑convergence workflow, and engineering safeguards to make the insights stable and reusable.

AIGame UnderstandingLLM

0 likes · 19 min read

How AI Shifts Recommendation Systems from Simply Pushing Items to Guiding User Choices

Uncle Fei's Miscellany

Jul 6, 2026 · Artificial Intelligence

Continuously Understanding Every User: Deep Dive into User Profile Architecture and Lifecycle

This article explains why user profiles are essential for recommendation systems, details their static, long‑term, short‑term, and embedding components, describes the full lifecycle from cold start to interest drift and decay, and outlines engineering practices, storage strategies, pipeline design, sequence modeling and emerging LLM‑based profiling.

LLMembeddinginterest modeling

0 likes · 37 min read

DataFunSummit

Jul 5, 2026 · Artificial Intelligence

Designing Next‑Gen Recommendation and Search with Agentic Architectures

The article analyzes cutting‑edge AI search and recommendation techniques—including Alibaba Cloud's Agentic RAG, Huawei Noah's LLM‑enhanced recommender, and Baidu's generative ranking model—detailing their architectures, multi‑modal retrieval strategies, performance gains, and practical deployment insights.

AI SearchAgentic RAGAlibaba Cloud

0 likes · 6 min read

Designing Next‑Gen Recommendation and Search with Agentic Architectures

DataFunSummit

Jul 3, 2026 · Artificial Intelligence

Designing Next‑Gen Recommendation and Search Systems with Agentic Architectures

This article reviews cutting‑edge AI search and recommendation techniques—including Alibaba Cloud’s Agentic RAG, Huawei’s LLM‑enhanced recommendation pipeline, and Baidu’s generative ranking model GRAB—detailing their architectural evolution, multimodal retrieval strategies, performance benchmarks, and practical deployment insights.

AI SearchAgentic RAGGPU acceleration

0 likes · 6 min read

Designing Next‑Gen Recommendation and Search Systems with Agentic Architectures

Alibaba International Intelligent Technology

Jul 3, 2026 · Artificial Intelligence

How Generative Pretraining Overcomes Discriminative Model Bottlenecks in Ad Ranking

The article presents UserLLM, a three‑stage framework—generative pre‑training, discriminative SFT, and CTR fusion—that leverages ultra‑long user behavior sequences to address the compression and scaling limits of traditional discriminative ranking models, demonstrating significant offline gains and validated scaling‑law behavior across token, layer, and width dimensions.

UserLLMad rankinggenerative pretraining

0 likes · 17 min read

How Generative Pretraining Overcomes Discriminative Model Bottlenecks in Ad Ranking

Machine Heart

Jun 25, 2026 · Artificial Intelligence

From Finding to Generating Videos: How Kuaishou’s RaG Transforms Recommendation Systems

Kuaishou’s new Recommendation-as-Generation (RaG) framework replaces traditional retrieve-and-rank with a generative pipeline that predicts user interests, creates personalized video content, and closes the loop with feedback, delivering a 1.87% ad‑revenue lift for over 400 million daily users.

A/B testingLarge-Scale DeploymentSemantic IDs

0 likes · 14 min read

From Finding to Generating Videos: How Kuaishou’s RaG Transforms Recommendation Systems

Bilibili Tech

Jun 17, 2026 · Artificial Intelligence

LeAP: Precise Model Slimming with Learnable Adaptive Permutation for Feature Selection

LeAP replaces costly permutation‑based feature importance with an end‑to‑end learnable module that shuffles features, applies adaptive gradient‑bias regularization, and achieves state‑of‑the‑art sparsity and inference speedups on large‑scale recommender models.

LEAPadaptive permutationdeep learning

0 likes · 13 min read

LeAP: Precise Model Slimming with Learnable Adaptive Permutation for Feature Selection

Fei's Miscellaneous Talks

Jun 15, 2026 · Artificial Intelligence

Evolution of Recommendation System Architecture: From Hand‑Crafted Rules to the Large‑Model Era

This article traces the five‑generation evolution of recommendation algorithms—from manual rules, through LR and GBDT, to deep learning and large models—explaining each stage’s suitable scenarios, engineering upgrades, trade‑offs, and why practical architecture choices should prioritize business needs over chasing the newest technology.

Algorithm EvolutionGBDTdeep learning

0 likes · 32 min read

Evolution of Recommendation System Architecture: From Hand‑Crafted Rules to the Large‑Model Era

DataFunSummit

Jun 12, 2026 · Artificial Intelligence

How Agentic Architectures Power Next‑Gen Recommendation and Search Systems

This article analyzes cutting‑edge AI search and recommendation technologies, covering Alibaba Cloud's Agentic RAG architecture, Huawei Noah's LLM‑enhanced recommender evolution, and Baidu's generative ranking model GRAB, each with detailed designs, performance metrics, and real‑world deployment insights.

AI agentsGenerative RankingMultimodal Retrieval

0 likes · 6 min read

Alibaba International Intelligent Technology

Jun 12, 2026 · Artificial Intelligence

Personalized World Knowledge Lets Large Models Truly Understand Users in Generative Recommendation

This article introduces LWGR, a framework that uses personalized soft prompts generated by parallel codebooks and Lagrangian‑constrained knowledge fusion to integrate large language model world knowledge into generative recommendation, overcoming fixed‑prompt limitations and knowledge‑behavior conflicts, and demonstrates superior performance on public and industrial datasets with notable revenue gains in online A/B tests.

Knowledge FusionLagrangian constraintgenerative recommendation

0 likes · 11 min read

Personalized World Knowledge Lets Large Models Truly Understand Users in Generative Recommendation

DataFunSummit

Jun 11, 2026 · Artificial Intelligence

Designing Next‑Gen Recommendation and Search with Agentic Architectures

This article reviews cutting‑edge AI search and recommendation techniques—including Alibaba Cloud's Agentic RAG, Huawei Noah's LLM‑enhanced recommender, and Baidu's generative ranking model GRAB—detailing their architectures, multi‑modal retrieval strategies, performance gains, and practical deployment insights.

AI SearchAgentic RAGBaidu GRAB

0 likes · 6 min read

Machine Learning Algorithms & Natural Language Processing

Jun 10, 2026 · Artificial Intelligence

OneReason: Enabling Recommendation Systems to Reason

OneReason introduces a systematic reasoning capability into industrial recommendation models through multi‑stage pre‑training, chain‑of‑thought fine‑tuning, and reinforcement learning, achieving significant gains in click‑through, revenue, and cross‑domain recommendation performance while preserving the underlying language abilities of the base model.

PretrainingReinforcement Learningchain-of-thought

0 likes · 29 min read

OneReason: Enabling Recommendation Systems to Reason

DataFunSummit

Jun 8, 2026 · Artificial Intelligence

Agent Architecture in Action: Building Next‑Gen Recommendation and Search Systems

The article reviews cutting‑edge technical practices for next‑generation recommendation and search, covering Alibaba Cloud AI Search's Agentic RAG multi‑agent design, Huawei Noah's LLM‑enhanced recommendation evolution, Baidu's generative ranking (GRAB) for ads, and Elasticsearch‑based vector RAG implementations, with concrete architecture details and performance results.

AI SearchAgentic RAGElasticsearch

0 likes · 6 min read

Agent Architecture in Action: Building Next‑Gen Recommendation and Search Systems

Alibaba International Intelligent Technology

Jun 5, 2026 · Artificial Intelligence

From Alchemist to Decision‑Maker: How Agents Redefine Algorithm Engineers’ Role

The article details a multi‑layer Research Agent system that automates the repetitive execution steps of recommendation model development, demonstrates a 17.5% HitRate@100 lift on Lazada’s generative recall pipeline, and argues that while agents excel at efficient trial‑and‑error, true creative breakthroughs still require human insight.

AI agentsLazadaMulti-Agent Architecture

0 likes · 23 min read

From Alchemist to Decision‑Maker: How Agents Redefine Algorithm Engineers’ Role

Alibaba International Intelligent Technology

May 29, 2026 · Artificial Intelligence

Turning Cutting-Edge AI into a Cocktail at Zhejiang University’s Academic Bar

Alibaba International hosted an Academic Bar at Zhejiang University where experts presented multilingual e‑LLM models, next‑generation recommendation systems, AI‑driven optical design, safety frameworks for agents, a benchmark for e‑commerce chatbots, virtual user research, an Agent Harness architecture, and edge‑side large‑model techniques, illustrating the practical convergence of cutting‑edge AI research and industry.

AI agentsAI safetyEdge AI

0 likes · 9 min read

Turning Cutting-Edge AI into a Cocktail at Zhejiang University’s Academic Bar

Alimama Tech

May 28, 2026 · Artificial Intelligence

13 KDD'26 Papers from Taobao: Scaling Laws, World Models and New AI Paradigms

The article highlights thirteen Taobao‑group papers accepted at KDD 2026, covering large‑model scaling laws, end‑to‑end generative recommendation, CTR prediction, interactive recommendation agents, LLM‑based pricing, robust auto‑bidding, two‑stage auctions, generative world models, multi‑attribution conversion, uplift modeling and long‑term causal estimation for e‑commerce systems.

CTR predictionGenerative ModelsKDD 2026

0 likes · 29 min read

13 KDD'26 Papers from Taobao: Scaling Laws, World Models and New AI Paradigms

DataFunSummit

May 23, 2026 · Artificial Intelligence

Designing Next‑Gen Recommendation and Search Systems with Agentic Architectures

The article analyzes cutting‑edge AI search and recommendation technologies—including Alibaba Cloud's Agentic RAG, Huawei Noah's LLM‑enhanced recommendation pipeline, and Baidu's generative ranking model GRAB—detailing their architectural evolution, multi‑modal retrieval strategies, GPU acceleration gains, and measured performance improvements.

AI SearchAgentic RAGGPU acceleration

0 likes · 5 min read

Alibaba International Intelligent Technology

May 22, 2026 · Artificial Intelligence

How SORT Transforms Precision Ranking with a Transformer‑Based Architecture

SORT re‑architects industrial‑scale ranking by shifting to a request‑centric data paradigm, integrating sparse and MoE optimizations into a Transformer backbone, and delivering significant CTR‑AUC, FLOPs, and online metric improvements while maintaining high training and inference efficiency.

Large Model ScalingMixture of ExpertsTransformer

0 likes · 19 min read

How SORT Transforms Precision Ranking with a Transformer‑Based Architecture

DataFunSummit

May 21, 2026 · Artificial Intelligence

Designing Next‑Gen Recommendation and Search with Intelligent Agent Architecture

The article reviews a collection of technical chapters that analyze how multi‑agent AI architectures, large‑language‑model‑enhanced recommendation pipelines, generative ranking for ads, and Elasticsearch‑based vector RAG are applied to build next‑generation recommendation and search systems, citing concrete designs, performance numbers and real‑world deployments.

AI agentsElasticsearchGenerative Ranking

0 likes · 6 min read

Designing Next‑Gen Recommendation and Search with Intelligent Agent Architecture

DataFunSummit

May 19, 2026 · Artificial Intelligence

Designing Next‑Gen Recommendation and Search with Agentic RAG Architecture

The article reviews cutting‑edge AI techniques for high‑concurrency, multimodal recommendation and search, detailing Alibaba Cloud's Agentic RAG evolution, Huawei Noah's LLM‑enhanced recommendation pipeline, and Baidu's generative ranking model GRAB, each with architecture diagrams, performance metrics, and real‑world deployment insights.

AI agentsAgentic RAGGenerative Ranking

0 likes · 6 min read

Designing Next‑Gen Recommendation and Search with Agentic RAG Architecture

DataFunSummit

May 17, 2026 · Artificial Intelligence

How Agentic Architecture Powers Next‑Generation Recommendation and Search Systems

The article reviews cutting‑edge AI search and recommendation techniques—including Alibaba Cloud's Agentic RAG, Huawei Noah's LLM‑enhanced recommender, Baidu's generative ranking model GRAB, and Elasticsearch‑based vector RAG—detailing their challenges, architectural evolutions, performance gains, and real‑world deployment results.

AI SearchAgentic RAGElasticsearch

0 likes · 6 min read

How Agentic Architecture Powers Next‑Generation Recommendation and Search Systems

DataFunSummit

May 8, 2026 · Artificial Intelligence

Agent Architecture in Action: Building Next‑Gen Recommendation and Search Systems

This article reviews cutting‑edge AI search and recommendation technologies, covering Alibaba Cloud's Agentic RAG architecture, Huawei Noah's LLM‑enhanced recommendation pipeline, and Baidu's generative ranking model GRAB, while detailing their design challenges, multi‑modal retrieval strategies, performance gains, and real‑world deployment results.

AI SearchAgentic RAGGenerative Ranking

0 likes · 6 min read

DataFunSummit

May 5, 2026 · Artificial Intelligence

How Huawei Noah’s KAR Project Leverages LLMs to Advance Recommendation Systems

The article reviews the evolution of recommendation systems from deep learning to large language models, analyzes core challenges such as noisy implicit feedback and limited semantic understanding, and details Huawei Noah’s KAR solution that uses factorized prompting, multi‑expert adapters, and AI‑Agent architectures to achieve a 1.5% AUC lift and validated online A/B test results.

AI AgentAUCHuawei

0 likes · 5 min read

How Huawei Noah’s KAR Project Leverages LLMs to Advance Recommendation Systems

DataFunTalk

May 5, 2026 · Artificial Intelligence

Agent Architecture in Action: Building Next‑Gen Recommendation and Search Systems

This article reviews cutting‑edge AI search and recommendation techniques—including Alibaba Cloud's Agentic RAG, Huawei Noah's LLM‑enhanced recommendation pipeline, and Baidu's generative ranking model GRAB—detailing their architectural evolution, multimodal retrieval strategies, GPU acceleration, and measured performance gains.

AI SearchAgentic RAGGPU acceleration

0 likes · 6 min read

DataFunSummit

May 1, 2026 · Artificial Intelligence

How Agentic Architectures Power the Next‑Gen Recommendation and Search Systems

This article summarizes a technical ebook that analyzes the evolution of recommendation and search systems—from deep‑learning models to large‑language‑model agents—detailing multi‑agent RAG architectures, Huawei’s KAR knowledge adapters, Baidu’s generative ranking (GRAB), Elasticsearch vector search, and performance results such as a 1.5% AUC lift and GPU‑accelerated throughput gains.

ElasticsearchGenerative RankingMulti-Agent Architecture

0 likes · 6 min read

How Agentic Architectures Power the Next‑Gen Recommendation and Search Systems

Subtle Storm

Apr 27, 2026 · Big Data

When Algorithms Learn to Read People: Big Data’s Commercial Value in E‑Commerce (Part 1)

The article explains how big‑data techniques such as collaborative filtering, time‑aware recommendations, and behavior‑sequence profiling transform e‑commerce and retail by revealing individual consumer intent, boosting conversion rates by up to 30% and enabling more precise pricing and inventory decisions.

behavior analysisbig datae-commerce

0 likes · 5 min read

When Algorithms Learn to Read People: Big Data’s Commercial Value in E‑Commerce (Part 1)

Kuaishou Tech

Apr 24, 2026 · Artificial Intelligence

ICLR 2026: Kuaishou Tech Team’s Cutting‑Edge AI Research Highlights

This article reviews eight Kuaishou‑authored papers accepted at ICLR 2026, summarizing their problem statements, novel methods such as front‑door causal attribution, visual table retrieval, denoising rerankers, difficulty‑adaptive reasoning, diffusion code infilling, generative ordinal regression, multimodal video retrieval, e‑commerce dialogue benchmarks, and a new LLM creativity evaluator, together with reported experimental gains.

Artificial IntelligenceICLR 2026Kuaishou

0 likes · 19 min read

ICLR 2026: Kuaishou Tech Team’s Cutting‑Edge AI Research Highlights

Alibaba International Intelligent Technology

Apr 24, 2026 · Artificial Intelligence

Alibaba International AI Team Lands Multiple Papers at SIGIR, WWW, and WSDM 2026

Alibaba International Intelligent Technology showcases nine industrial‑grade recommendation and search papers accepted at SIGIR, WWW, and WSDM 2026, detailing sparse scaling, counterfactual multi‑task learning, generative recommendation, MoE routing, and multimodal semantic ID breakthroughs with extensive offline and online results.

Mixture of Expertscounterfactual learninge‑commerce AI

0 likes · 24 min read

Alibaba International AI Team Lands Multiple Papers at SIGIR, WWW, and WSDM 2026

DataFunSummit

Apr 21, 2026 · Industry Insights

How AI Search & Recommendation Systems Beat Multi-Modal, High-Concurrency Hurdles

This article reviews cutting‑edge technical practices from Alibaba Cloud AI Search, Huawei Noah's recommendation platform, and Baidu's GRAB model, detailing how multi‑agent RAG architectures, large‑language‑model enhancements, and generative ranking overcome high‑concurrency, multi‑modal data, and feature‑engineering bottlenecks.

AI SearchGenerative RankingMulti-Modal Retrieval

0 likes · 6 min read

How AI Search & Recommendation Systems Beat Multi-Modal, High-Concurrency Hurdles

Alimama Tech

Mar 26, 2026 · Industry Insights

How Alibaba’s Large User Model (LUM) Boosted CTR by 4.5% and Scaled to Billions of Parameters

The article analyzes the evolution from traditional modular recommendation models to a generative Large User Model (LUM), detailing its three‑stage paradigm, tokenization, training objectives, scaling‑law findings, offline and online experiments, and the AI‑infra innovations that enabled a 4.5% CTR lift in production.

CTR predictionGenerative Modelinglarge language models

0 likes · 18 min read

How Alibaba’s Large User Model (LUM) Boosted CTR by 4.5% and Scaled to Billions of Parameters

Tencent Advertising Technology

Mar 23, 2026 · Industry Insights

Why Tencent’s $885K KDD Cup Challenge Could Redefine Recommendation Systems

The 2026 KDD Cup, powered by Tencent’s Advertising Algorithm Competition with an $885,000 prize pool, challenges participants to unify sequence modeling and feature interaction in large‑scale recommendation systems, offering academic publication paths, real‑world deployment opportunities, and strict latency constraints that push both research and engineering innovation.

AICompetitionKDD Cup

0 likes · 16 min read

Why Tencent’s $885K KDD Cup Challenge Could Redefine Recommendation Systems

AI Explorer

Mar 20, 2026 · Industry Insights

Key AI Breakthroughs and Market Moves on March 20 2026

On March 20 2026, Alibaba’s Qwen 3.5‑Max topped the LMArena blind‑test, OpenAI bought Astral to boost AI coding, Zhejiang University released a real‑time 4D world model, Meta’s Agent leaked data, and a series of AI‑driven innovations from Nvidia, robotics to drug discovery reshaped the industry.

AIAI design toolsAI hardware

0 likes · 7 min read

Key AI Breakthroughs and Market Moves on March 20 2026

Kuaishou Tech

Mar 4, 2026 · Artificial Intelligence

How LLMs Are Revolutionizing Reinforcement Learning for Recommendation Systems

This survey examines the emerging LLM‑RL collaborative recommendation paradigm, outlining its research background, five main collaboration patterns, standardized evaluation protocols, and the key challenges and future directions for building smarter, more robust recommender systems.

Artificial IntelligenceLLMReinforcement Learning

0 likes · 14 min read

How LLMs Are Revolutionizing Reinforcement Learning for Recommendation Systems

AI Explorer

Mar 3, 2026 · Industry Insights

How Meta’s Social Graph Could Redefine E‑Commerce Recommendations

Meta is secretly testing an AI shopping assistant that leverages billions of users' social profiles, shifting recommendation logic from reactive behavior data to proactive identity‑driven suggestions, while raising significant privacy and ecosystem implications for e‑commerce.

AIMetaSocial Graph

0 likes · 6 min read

How Meta’s Social Graph Could Redefine E‑Commerce Recommendations

DataFunSummit

Feb 27, 2026 · Artificial Intelligence

How Large Language Models Are Revolutionizing Ad Recommendation and Solving Cold‑Start Problems

This article explains how advertising recommendation is evolving from traditional feature‑engineered models to LLM‑driven pipelines, detailing data‑infrastructure challenges, semantic upgrades with multimodal embeddings, case studies in short‑video ads, user cold‑start prompt engineering, and future directions for generative recommendation systems.

Ad TechLLMMultimodal

0 likes · 12 min read

How Large Language Models Are Revolutionizing Ad Recommendation and Solving Cold‑Start Problems

Alibaba International Intelligent Technology

Feb 27, 2026 · Artificial Intelligence

A New Dual‑Tower Multi‑Objective Recall Framework (CSMF) Boosts Efficiency Without Extra Parameters

The paper introduces CSMF, a cascaded selective‑mask fine‑tuning framework for dual‑tower embedding‑based retrieval that sequentially optimizes exposure, click and conversion goals, incorporates cumulative percentile pruning and adaptive margin loss, and achieves measurable gains in ad revenue and CTR without increasing model size or latency.

cascaded selective maskdual-towerembedding-based retrieval

0 likes · 15 min read

A New Dual‑Tower Multi‑Objective Recall Framework (CSMF) Boosts Efficiency Without Extra Parameters

DeWu Technology

Feb 11, 2026 · Artificial Intelligence

How Generative Models Transform Re‑ranking Architecture for Faster, More Diverse Recommendations

This article examines the evolution of re‑ranking systems from traditional pointwise models to a two‑stage generation‑evaluation framework, compares autoregressive and non‑autoregressive generative approaches, details inference speed optimizations with GPU and model‑server upgrades, and outlines a future end‑to‑end sequence generation architecture enhanced by reinforcement learning and contrastive learning.

AIGenerative ModelsReinforcement Learning

0 likes · 14 min read

How Generative Models Transform Re‑ranking Architecture for Faster, More Diverse Recommendations

Ximalaya Technology Team

Feb 11, 2026 · Artificial Intelligence

How Ximalaya Used Generative AI to Revolutionize Audio Recommendations

This article details Ximalaya's journey from traditional multi‑stage recommendation pipelines to generative AI‑driven models, covering business challenges, architectural and model differences, phased deployments, knowledge distillation, semantic ID encoding, decoder‑only strategies, extensive offline and online evaluations, and future research directions.

Encoder-DecoderReinforcement LearningSemantic ID

0 likes · 24 min read

How Ximalaya Used Generative AI to Revolutionize Audio Recommendations

Data Party THU

Feb 9, 2026 · Artificial Intelligence

Aligning Collaborative Filtering with LLM Token Generation: The TCA4Rec Breakthrough

This paper introduces the TCA4Rec framework that directly aligns item‑level collaborative‑filtering preferences with token‑level objectives of large language models, presenting novel modules, extensive experiments, and analysis that demonstrate significant performance gains in generative recommendation tasks.

LLMTCA4RecToken Alignment

0 likes · 9 min read

Aligning Collaborative Filtering with LLM Token Generation: The TCA4Rec Breakthrough

Network Intelligence Research Center (NIRC)

Jan 25, 2026 · Artificial Intelligence

RecFlow Breaks DLRM Inference Bottleneck with Fine-Grained GPU Parallelism

RecFlow, a new inference engine from Beijing University of Posts and Telecommunications and Meituan, tackles the resource mismatch of DLRM models by coordinating embedding and DNN operators at the intra‑SM level and introducing interference‑aware adaptive scheduling and incremental batching, achieving up to 9.34× higher throughput on RTX 3090.

DLRMFine-grained parallelismGPU acceleration

0 likes · 7 min read

RecFlow Breaks DLRM Inference Bottleneck with Fine-Grained GPU Parallelism

Kuaishou Tech

Jan 19, 2026 · Artificial Intelligence

How OneSug Revolutionizes E‑commerce Query Suggestion with End‑to‑End Generative Modeling

OneSug introduces an end‑to‑end generative framework that unifies recall, coarse‑ranking, and fine‑ranking for e‑commerce query suggestion, addressing the limitations of traditional multi‑stage cascades and dramatically improving relevance, efficiency, and business metrics in real‑world deployments.

Generative Modelse-commercemachine learning

0 likes · 10 min read

How OneSug Revolutionizes E‑commerce Query Suggestion with End‑to‑End Generative Modeling

Alibaba International Intelligent Technology

Jan 5, 2026 · Artificial Intelligence

Decoupled Multimodal Fusion (DMF) Boosts User Interest Modeling for CTR Prediction

The DMF framework introduces a modality‑enriched Decoupled Target Attention (DTA) and a Complementary Modality Modeling (CMM) strategy that together close the semantic gap between ID and multimodal features, delivering up to 5.3% CTCVR lift, 7.43% GMV increase, and a three‑fold throughput gain in large‑scale e‑commerce recommendation scenarios.

A/B testingCMMCTR prediction

0 likes · 13 min read

Decoupled Multimodal Fusion (DMF) Boosts User Interest Modeling for CTR Prediction

DataFunTalk

Jan 4, 2026 · Artificial Intelligence

How Agentic RAG and Generative Ranking Are Redefining AI Search and Recommendation

This article summarizes three cutting‑edge AI techniques—Alibaba Cloud's Agentic RAG architecture for multimodal search, Huawei Noah's large‑model‑driven recommendation system evolution, and Baidu's generative ranking (GRAB) model for ads—detailing their challenges, designs, performance gains, and practical deployment insights.

AI SearchGenerative RankingMulti-Agent Architecture

0 likes · 7 min read

How Agentic RAG and Generative Ranking Are Redefining AI Search and Recommendation

Network Intelligence Research Center (NIRC)

Dec 31, 2025 · Artificial Intelligence

Why AI Inference Is Slow and How Cutting‑Edge Tech Boosts It in Industrial Settings

The article analyzes the severe inference bottlenecks of large language models, CNNs, and recommendation systems and presents a suite of research‑driven accelerations—including token‑level pipeline parallelism (HPipe), KV‑cache clustering (ClusterAttn), quantization (QoKV), heterogeneous edge frameworks (DeepZoning, PICO), delay‑aware edge‑cloud scheduling (DECC), and operator choreography (RACE)—validated on real‑world industrial workloads.

AI inferenceEdge AIheterogeneous computing

0 likes · 16 min read

Why AI Inference Is Slow and How Cutting‑Edge Tech Boosts It in Industrial Settings

DataFunSummit

Dec 19, 2025 · Artificial Intelligence

How Agentic RAG, LLM‑Powered Recommendations, and Generative Ranking Transform AI Search and Ads

This article surveys cutting‑edge AI techniques—including Alibaba Cloud's Agentic RAG for multimodal search, Huawei Noah's LLM‑enhanced recommendation evolution, and Baidu's generative ranking (GRAB) for ads—detailing their architectures, optimization tricks, performance gains, and real‑world deployment results.

AI SearchAgentic RAGGPU acceleration

0 likes · 9 min read

How Agentic RAG, LLM‑Powered Recommendations, and Generative Ranking Transform AI Search and Ads

JD Retail Technology

Dec 11, 2025 · Artificial Intelligence

How GIN-Based Cohort Modeling Boosts Cold-Start CTR Prediction by 2%

This article explains a SIGIR 2025 paper that tackles cold‑start click‑through‑rate prediction in JD's ad system by using a Graph Isomorphism Network‑based cohort modeling framework, detailing its three‑module architecture, extensive experiments on public and industrial datasets, and a live deployment that achieved a 2.13% CTR lift.

CTR predictionGinGraph Neural Network

0 likes · 9 min read

How GIN-Based Cohort Modeling Boosts Cold-Start CTR Prediction by 2%

DataFunTalk

Dec 2, 2025 · Artificial Intelligence

How Agentic RAG, LLM‑Powered Recommendation, and Generative Ranking Are Redefining AI Search

This article reviews three cutting‑edge AI search and recommendation techniques—Alibaba Cloud's Agentic RAG architecture, Huawei Noah's LLM‑enhanced recommendation pipeline, and Baidu's GRAB generative ranking model—detailing their design challenges, multi‑modal retrieval strategies, performance gains, and real‑world deployment results.

AI SearchAI agentsGenerative Ranking

0 likes · 8 min read

How Agentic RAG, LLM‑Powered Recommendation, and Generative Ranking Are Redefining AI Search

Alibaba International Intelligent Technology

Dec 1, 2025 · Artificial Intelligence

Alibaba’s AI Team Sponsors CIKM 2025 and Launches the AnalyticCup Competition

The article reports that CIKM 2025 in Seoul featured three Alibaba International Intelligent Technology papers on multimodal recommendation and dynamic reserve pricing, and that the team organized the AnalyticCup competition and a workshop to advance multilingual e‑commerce search using large language models.

CIKM 2025Dynamic Reserve PriceMultilingual Search

0 likes · 8 min read

Alibaba’s AI Team Sponsors CIKM 2025 and Launches the AnalyticCup Competition

DataFunSummit

Nov 29, 2025 · Artificial Intelligence

How LLMs Are Transforming Long-Term Cross-Domain Interest Modeling for Recommendations

The Datafun Summit 2025 talk by JD’s algorithm engineer Tian Mingyang explains how generative AI is driving a paradigm shift in recommendation systems, detailing the limits of traditional models, the new dynamic cross‑domain inference chain technique, joint engineering‑algorithm optimizations, and the remaining challenges for future deployment.

AICross-Domain ModelingEngineering Optimization

0 likes · 32 min read

How LLMs Are Transforming Long-Term Cross-Domain Interest Modeling for Recommendations

DataFunTalk

Nov 25, 2025 · Artificial Intelligence

Unlocking Agentic RAG and Generative Ranking: AI Search & Recommendation Breakthroughs

This article summarizes cutting‑edge techniques from Alibaba Cloud AI Search’s Agentic RAG architecture, Huawei Noah’s LLM‑enhanced recommendation evolution, and Baidu’s GRAB generative ranking model, detailing multi‑agent retrieval, multimodal data handling, scaling laws, causal attention, and performance gains demonstrated through benchmarks and real‑world deployments.

AI SearchAgentic RAGGenerative Ranking

0 likes · 8 min read

Unlocking Agentic RAG and Generative Ranking: AI Search & Recommendation Breakthroughs

DataFunSummit

Nov 22, 2025 · Artificial Intelligence

Breaking the Recommendation Filter Bubble: Alibaba 1688’s Inference‑Driven AI

Alibaba’s 1688 platform leverages inference‑based large language models to enhance recommendation discovery, addressing the filter‑bubble problem by analyzing long‑term buyer behavior, compressing extensive activity streams, generating nuanced demand queries, and integrating multimodal data and market trend agents to deliver more diverse, explainable product suggestions for B‑type buyers.

AIE‑commerceUser Modeling

0 likes · 23 min read

Breaking the Recommendation Filter Bubble: Alibaba 1688’s Inference‑Driven AI

Alibaba International Intelligent Technology

Nov 21, 2025 · Artificial Intelligence

MMQ Advances Multimodal Fusion and Aligns Behavior for Large-Scale Recommendation Models

The paper introduces MMQ, a multimodal mixture‑of‑quantization semantic‑ID framework that compresses item multimodal features via shared‑specific expert networks and behavior‑aware fine‑tuning, achieving lower reconstruction loss, superior recall and ranking performance, and online gains of +1.29% REV, +4.33% CVR, +2.61% GMV, and +1.18% ROI.

MMQSemantic IDbehavior-aware fine-tuning

0 likes · 14 min read

MMQ Advances Multimodal Fusion and Aligns Behavior for Large-Scale Recommendation Models

DataFunSummit

Nov 9, 2025 · Artificial Intelligence

How Kuaishou Boosted Ad Performance with Multimodal LLMs: COPE & LEARN Frameworks

This article reviews Kuaishou's two‑year exploration of large‑model techniques in advertising, detailing the challenges of content‑domain ad estimation, the use of multimodal and LLM technologies to harness full‑scope user behavior and external knowledge, and the COPE and LEARN frameworks that delivered measurable business gains.

Advertisingknowledge transferlarge language models

0 likes · 6 min read

How Kuaishou Boosted Ad Performance with Multimodal LLMs: COPE & LEARN Frameworks

JD Tech

Nov 6, 2025 · Artificial Intelligence

LLMs Revolutionize Recommendation Systems: From Generative Models to Production

This article surveys the evolution of generative recommendation systems powered by large language models, detailing their technical foundations, engineering challenges, recent breakthroughs, and future research directions, while highlighting why the paradigm shift is occurring now.

AI EngineeringLLMgenerative recommendation

0 likes · 30 min read

LLMs Revolutionize Recommendation Systems: From Generative Models to Production

Zhihu Tech Column

Nov 4, 2025 · Artificial Intelligence

How Multimodal Large Models Transform Recommendation Systems: From Tags to Embeddings

This article explores how multimodal large models like Qwen2.5‑VL enable high‑dimensional tag generation and universal embeddings for recommendation systems, detailing data synthesis, model training, quantization, fine‑tuning, and the resulting improvements in click‑through rate and exposure interaction.

content taggingembeddinglarge language models

0 likes · 17 min read

How Multimodal Large Models Transform Recommendation Systems: From Tags to Embeddings

Kuaishou Large Model

Oct 31, 2025 · Artificial Intelligence

EMER: End-to-End Multi-Objective Ranking That Transforms Short-Video Recommendations

EMER, Kuaishou’s end‑to‑end multi‑objective ensemble ranking framework, replaces handcrafted scoring formulas with a transformer‑based model that learns comparative preferences, integrates normalized rank features, optimizes relative satisfaction and multi‑dimensional proxy metrics, and dynamically balances objectives via a self‑evolving advantage evaluator, delivering significant online gains.

Transformermachine learningmulti-objective ranking

0 likes · 17 min read

EMER: End-to-End Multi-Objective Ranking That Transforms Short-Video Recommendations

Kuaishou Tech

Oct 30, 2025 · Artificial Intelligence

How EMER Revolutionizes Short‑Video Ranking with End‑to‑End Multi‑Objective Learning

This article details the EMER framework—a Transformer‑based, end‑to‑end multi‑objective ranking system that replaces handcrafted formulas with a learnable AI model, introduces relative‑satisfaction signals and dynamic loss weighting, and demonstrates significant offline and online performance gains in Kuaishou's short‑video recommendation pipeline.

AImulti-objective learningonline experiments

0 likes · 16 min read

How EMER Revolutionizes Short‑Video Ranking with End‑to‑End Multi‑Objective Learning

Ele.me Technology

Oct 27, 2025 · Artificial Intelligence

How IAK Transforms Multi‑Domain Recommendation with Pre‑Training and Fine‑Tuning

This paper introduces IAK, a unified multi‑domain recommendation paradigm that treats the system as a large model, leveraging pre‑training and fine‑tuning with an information‑aware adaptive kernel to capture rapid user interest shifts while reducing training costs and improving online performance.

Online A/B TestingPretrainingfine‑tuning

0 likes · 18 min read

How IAK Transforms Multi‑Domain Recommendation with Pre‑Training and Fine‑Tuning

DataFunSummit

Oct 12, 2025 · Artificial Intelligence

How Kuaishou Uses Large Models to Supercharge Ad Targeting with COPE and LEARN

This article reviews Kuaishou's two‑year exploration of multimodal large‑model techniques for advertising, outlining challenges in content‑domain ad estimation, the COPE unified product representation framework, and the LEARN LLM knowledge‑transfer approach that together improve ad system performance.

AdvertisingKuaishouLLM

0 likes · 6 min read

How Kuaishou Uses Large Models to Supercharge Ad Targeting with COPE and LEARN

DataFunSummit

Oct 12, 2025 · Artificial Intelligence

How Baidu’s Generative Recall System (COBRA) Revolutionizes Ad Recommendations

This article details Baidu's generative recommendation ad recall framework, introducing the COBRA system and its three development stages—dense representation compression, sparse quantization with ID generation, and dense‑sparse cascading—highlighting coarse‑to‑fine inference, performance gains, long‑sequence extensions, online deployment, and future research directions.

Contrastive Learningad recallcobra

0 likes · 18 min read

How Baidu’s Generative Recall System (COBRA) Revolutionizes Ad Recommendations

DataFunSummit

Oct 10, 2025 · Artificial Intelligence

How Kuaishou Boosted Ad Performance with Multimodal Large Models

This article reviews Kuaishou's two‑year exploration of large‑model techniques in advertising, outlining challenges in content‑domain ad estimation, introducing the COPE unified content representation framework and the LEARN LLM knowledge‑transfer approach, and showing how these innovations delivered tangible business gains.

AIAdvertisingKuaishou

0 likes · 5 min read

How Kuaishou Boosted Ad Performance with Multimodal Large Models

DataFunSummit

Oct 9, 2025 · Artificial Intelligence

How Kuaishou Boosted Ad Performance with Multimodal Large Models: COPE & LEARN

This article reviews Kuaishou's two‑year exploration of multimodal large‑model techniques for advertising, detailing challenges of fragmented user behavior, the COPE unified product representation framework, and the LEARN LLM knowledge‑transfer approach that together delivered measurable business gains.

AIAdvertisingKuaishou

0 likes · 6 min read

How Kuaishou Boosted Ad Performance with Multimodal Large Models: COPE & LEARN

Alimama Tech

Oct 1, 2025 · Artificial Intelligence

How RecIS Revolutionizes Large‑Scale Sparse‑Dense Recommendation Training

RecIS is an open‑source, PyTorch‑based unified framework designed for ultra‑large‑scale sparse‑dense computation in recommendation systems, offering a full solution for training models with massive samples, multimodal inputs, and large embeddings, and demonstrating significant performance gains over TensorFlow and TorchRec in production deployments.

PyTorchdeep learning frameworklarge-scale AI

0 likes · 24 min read

How RecIS Revolutionizes Large‑Scale Sparse‑Dense Recommendation Training

DataFunSummit

Sep 30, 2025 · Artificial Intelligence

How Kuaishou Uses Large Models to Boost Ad Performance with COPE and LEARN

This article outlines Kuaishou's two‑year exploration of large‑model techniques in advertising, detailing challenges of sparse cross‑domain data, the COPE unified product representation framework, and the LEARN LLM knowledge‑transfer approach that together improve ad system effectiveness.

COPELLMMultimodal

0 likes · 6 min read

How Kuaishou Uses Large Models to Boost Ad Performance with COPE and LEARN

DataFunSummit

Sep 30, 2025 · Artificial Intelligence

How Kuaishou Boosted Ad Performance with Multimodal LLMs: COPE & LEARN Frameworks

Over the past two years, Kuaishou has leveraged multimodal large‑model techniques to overcome sparse advertising data, integrating full‑domain user behavior and external knowledge via the COPE unified product representation framework and the LEARN LLM knowledge‑transfer system, achieving measurable business gains.

KuaishouLLMMultimodal

0 likes · 6 min read

HyperAI Super Neural

Sep 30, 2025 · Artificial Intelligence

OnePiece: Applying LLM‑Style Reasoning to Item‑ID Sequences for Generative Recommendation

The article presents the OnePiece framework, which injects LLM‑style context engineering and latent reasoning into item‑ID based search‑and‑recommendation models, details the design choices, training tricks, attention analysis, and reports online gains of around 1% GMV and ad revenue, offering a thorough technical dissection of generative recommendation in industrial settings.

LLM reasoningOnePieceTransformer

0 likes · 31 min read

OnePiece: Applying LLM‑Style Reasoning to Item‑ID Sequences for Generative Recommendation

DataFunSummit

Sep 26, 2025 · Artificial Intelligence

How Large Language Models are Transforming Recommendation Systems: Insights from Huawei

This article reviews Huawei Noah's Ark Lab's exploration of large language models in recommendation systems, covering background challenges, the KAR and Uni-CTR projects, experimental results, and future research directions for open, knowledge‑driven recommendation pipelines.

AI researchHuaweiknowledge-assisted recommendation

0 likes · 13 min read

How Large Language Models are Transforming Recommendation Systems: Insights from Huawei

DataFunSummit

Sep 17, 2025 · Product Management

How AI Is Redefining Recommendation Strategies and Product Management Careers

This presentation explores AI-era recommendation strategies, defines strategy product roles and ability models, outlines the three generations of product managers, discusses AI-driven trends, workflow simplifications, 2024 observations, and offers practical guidance for career growth in product management.

AIProduct Managementlarge models

0 likes · 14 min read

How AI Is Redefining Recommendation Strategies and Product Management Careers

DataFunTalk

Sep 12, 2025 · Artificial Intelligence

How Large Language Models Are Transforming Health E‑Commerce Recommendations

This article explains how JD Health’s recommendation team integrates large‑model technologies—scaling CTR models, enhancing pipelines with LLMs, and adopting generative models—into e‑commerce recommendation systems, highlighting practical applications and technical challenges specific to the health‑commerce sector.

AICTR modelse-commerce

0 likes · 5 min read

How Large Language Models Are Transforming Health E‑Commerce Recommendations

DataFunSummit

Sep 9, 2025 · Artificial Intelligence

How Baidu’s GRAB Model Uses Scaling Laws to Transform Ad Ranking

This article explains Baidu's generative ranking model GRAB, detailing how scaling laws from large language models inspire a new recommendation paradigm, the model's architecture, custom attention mechanisms, training strategies, deployment optimizations, and the resulting business gains in CTR and revenue.

BaiduCTR predictiongenerative AI

0 likes · 22 min read

How Baidu’s GRAB Model Uses Scaling Laws to Transform Ad Ranking

JD Tech Talk

Sep 9, 2025 · Artificial Intelligence

How JD’s Dynamic Re‑Ranking Model Boosted Search Relevance and Won SIGIR 2024

The author recounts how, by modeling user intent with a multi‑layer Gaussian‑based PODM‑MI framework and addressing a novel ‘sand‑glass’ bottleneck in RQ‑VAE semantic identifiers, JD’s search ranking achieved significant UCVR gains, annual order increases of over ten million, and a SIGIR 2024 paper acceptance.

SIGIRdistribution modelinge‑commerce AI

0 likes · 8 min read

How JD’s Dynamic Re‑Ranking Model Boosted Search Relevance and Won SIGIR 2024

Data Party THU

Aug 14, 2025 · Artificial Intelligence

How FilterLLM Turns One LLM Pass into Billion‑User Cold‑Start Recommendations

The article analyzes the FilterLLM approach, which augments a frozen LLM with billions of learnable user tokens to predict a full‑user interaction probability distribution in a single forward pass, dramatically speeding up cold‑start recommendation while preserving recommendation quality across multiple benchmarks.

AIFilterLLMLLM

0 likes · 8 min read

How FilterLLM Turns One LLM Pass into Billion‑User Cold‑Start Recommendations

Kuaishou Tech

Jul 29, 2025 · Artificial Intelligence

How Kuaishou’s 8 Groundbreaking Papers Are Shaping AI at KDD 2025

Eight Kuashou research papers covering recommendation systems, multi‑task learning, multimodal large models, large language models, and combinatorial optimization have been accepted to the premier AI data‑mining conference KDD 2025, highlighting the company’s cutting‑edge innovations and their potential impact on the field.

AIMultimodal Learningdata mining

0 likes · 18 min read

How Kuaishou’s 8 Groundbreaking Papers Are Shaping AI at KDD 2025

Kuaishou Tech

Jul 23, 2025 · Artificial Intelligence

Revolutionizing Cascade Ranking with LCRON: End-to-End Training for Ads

This article introduces LCRON, a novel end-to-end training framework for cascade ranking systems that aligns training objectives with overall recall, addresses stage interaction challenges, and demonstrates significant performance gains on public benchmarks and in Kuaishou’s commercial advertising platform.

Advertisingcascade rankingend-to-end training

0 likes · 14 min read

Revolutionizing Cascade Ranking with LCRON: End-to-End Training for Ads

DataFunSummit

Jul 9, 2025 · Artificial Intelligence

How LAST Enables Real‑Time Learning for Re‑Ranking in E‑Commerce Recommendations

This article presents LAST, a novel Learning-at-Serving-Time approach that enables real‑time online learning for re‑ranking in industrial recommendation pipelines, eliminating feedback latency, detailing its architecture, challenges, experimental validation, and practical advantages over traditional online learning methods.

LAST algorithmonline servingre-ranking

0 likes · 12 min read

How LAST Enables Real‑Time Learning for Re‑Ranking in E‑Commerce Recommendations

DataFunTalk

Jul 3, 2025 · Artificial Intelligence

How Vivo’s Blue Heart XiaoV Leverages LLMs to Transform Conversational Recommendations

In an interview with Vivo AI engineer Liang Tianan, the article explores the challenges of post‑Q&A recommendation, the integration of large language models into recall, ranking and evaluation pipelines, and the engineering trade‑offs required to deliver high‑quality, diverse suggestions on mobile devices.

LLMMultimodaldialogue AI

0 likes · 15 min read

How Vivo’s Blue Heart XiaoV Leverages LLMs to Transform Conversational Recommendations

DataFunTalk

Jun 27, 2025 · Artificial Intelligence

How Generative AI is Revolutionizing Ad Recommendation Systems

Join Baidu senior algorithm engineer Ji Zhi at the DataFun Summit 2025 to explore how generative AI transforms ad recommendation recall, covering item representation, evolving solution architectures, long‑sequence challenges, and practical insights for building efficient large‑model recommendation systems.

AI researchAd TechBaidu

0 likes · 3 min read

How Generative AI is Revolutionizing Ad Recommendation Systems

DataFunSummit

Jun 20, 2025 · Artificial Intelligence

EasyRec Deep Dive: Training & Inference Architecture, Optimizations, and Online Learning

This article explains EasyRec's end‑to‑end recommendation system, covering its training‑inference architecture, a series of CPU/GPU and distributed optimizations, and a real‑time online‑learning pipeline that together improve throughput, latency, and model freshness.

AI infrastructureDistributed Computinginference optimization

0 likes · 15 min read

EasyRec Deep Dive: Training & Inference Architecture, Optimizations, and Online Learning

Meituan Technology Team

May 15, 2025 · Artificial Intelligence

How Meituan’s MTGR Framework Achieved 65× Faster Inference with Scaling Laws

Meituan’s recommendation team introduced the MTGR framework, aligning traditional DLRM features with a unified HSTU‑based Transformer to explore scaling laws, delivering a 65‑fold FLOPs boost, 12% lower inference cost, and record gains in online CTR and order volume across its food‑delivery platform.

Large‑Scale TrainingMTGRTransformer

0 likes · 26 min read

How Meituan’s MTGR Framework Achieved 65× Faster Inference with Scaling Laws