Tagged articles

Recommendation Systems

489 articles · Page 2 of 5

Feb 14, 2025 · Artificial Intelligence

Building Large‑Scale Recommendation Systems with Big Data and Large Language Models on Alibaba Cloud AI Platform

This presentation details how Alibaba Cloud's AI platform integrates big‑data pipelines, feature‑store services, and large language model capabilities to construct high‑performance search‑recommendation architectures, covering system design, training and inference optimizations, LLM‑driven use cases, and open‑source RAG tooling.

AI platformBig DataDistributed Training

0 likes · 17 min read

Building Large‑Scale Recommendation Systems with Big Data and Large Language Models on Alibaba Cloud AI Platform

ByteDance Data Platform

Feb 12, 2025 · Fundamentals

Why A/B Tests Fail in Recommendation Systems and How to Fix Them

This article examines the hidden complexities of A/B experiments in short‑video recommendation feeds, explains why traditional designs produce biased results due to learning, double‑sided, and network effects, and presents practical double‑sided and community‑randomized experiment frameworks to obtain unbiased strategy evaluations.

A/B testingCommunity randomizationDouble-sided effects

0 likes · 21 min read

Why A/B Tests Fail in Recommendation Systems and How to Fix Them

JD Retail Technology

Feb 12, 2025 · Artificial Intelligence

Accelerating Generative Recommendation with NVIDIA TensorRT‑LLM in JD Advertising

JD Advertising accelerates its generative‑recall recommendation system by integrating NVIDIA TensorRT‑LLM, which simplifies the pipeline, injects LLM knowledge, scales to billions of parameters, and delivers over five‑fold throughput gains, one‑fifth the cost, and significant CTR improvements in both recommendation and search.

LLMRecommendation SystemsTensorRT-LLM

0 likes · 13 min read

Accelerating Generative Recommendation with NVIDIA TensorRT‑LLM in JD Advertising

DataFunTalk

Feb 6, 2025 · Artificial Intelligence

Why Graph Neural Networks Are Suitable for Recommendation Systems

Graph Neural Networks excel in recommendation systems because they can model complex user‑item relationships, capture high‑order interactions, adapt dynamically to real‑time behavior, propagate multi‑step information, enrich contextual embeddings, alleviate data sparsity, and improve long‑tail item coverage, with practical e‑commerce case studies available for download.

Artificial IntelligenceGNNRecommendation Systems

0 likes · 5 min read

Why Graph Neural Networks Are Suitable for Recommendation Systems

JD Retail Technology

Jan 21, 2025 · Artificial Intelligence

Tech Insight: Selected JD Retail Technology Papers in Artificial Intelligence (2024)

Tech Insight highlights ten 2024 JD Retail Technology AI papers presented at top conferences—including CVPR, SIGIR, WWW, AAAI and IJCAI—that advance open‑vocabulary object detection, unified search‑recommendation, pre‑ranking consistency, diversity‑aware re‑ranking, a diversified product‑search dataset, graph‑based query classification, plug‑in CTR models, parallel ad‑ranking, trajectory‑based CTR stability, and task‑aware decoding for large language models.

Artificial IntelligenceCTR predictionE‑commerce

0 likes · 20 min read

Tech Insight: Selected JD Retail Technology Papers in Artificial Intelligence (2024)

ZhongAn Tech Team

Jan 19, 2025 · Artificial Intelligence

Weekly AI Digest Issue 11: Recommendation Algorithms, Video Generation Advances, and AGI Research

This issue of the weekly AI digest explores Xiaohongshu’s NoteLLM recommendation system, compares Chinese text generation in video AI across major platforms, highlights Alibaba’s Tongyi Wanxiang breakthroughs, discusses Keras founder François Chollet’s new AGI‑focused lab, and reviews Google’s Veo 2 and Imagen‑3 advancements.

AGIAIRecommendation Systems

0 likes · 11 min read

Weekly AI Digest Issue 11: Recommendation Algorithms, Video Generation Advances, and AGI Research

DataFunTalk

Jan 18, 2025 · Artificial Intelligence

Understanding Xiaohongshu’s Content Recommendation Mechanisms: NoteLLM and SSD

This article analyzes Xiaohongshu’s content recommendation system by reviewing two official papers, detailing the NoteLLM framework for interest discovery and the Sliding Spectrum Decomposition (SSD) method for diversified recommendations, and explaining their underlying models, loss functions, and experimental results.

Collaborative FilteringDiversityLLM

0 likes · 13 min read

Understanding Xiaohongshu’s Content Recommendation Mechanisms: NoteLLM and SSD

Kuaishou Tech

Jan 17, 2025 · Artificial Intelligence

Kuaishou Achieves 7 Papers Accepted at AAAI 2025

Kuaishou has achieved a significant milestone with 7 papers accepted at AAAI 2025, covering diverse AI research areas including video processing, recommendation systems, and image restoration, demonstrating the company's strong research capabilities in artificial intelligence.

AAAI 2025Artificial IntelligenceKuaishou

0 likes · 10 min read

Kuaishou Achieves 7 Papers Accepted at AAAI 2025

JD Cloud Developers

Jan 14, 2025 · Artificial Intelligence

How Generative Recommendation Systems Transform E‑Commerce with LLMs

This article explains how large language models reshape recommendation systems by simplifying pipelines, integrating world knowledge, and leveraging scaling laws, and details the engineering steps for deploying generative recall models—including product encoding, user prompting, model training, TensorRT‑LLM optimization, and continuous performance improvements.

Generative RecommendationLLMRecommendation Systems

0 likes · 13 min read

How Generative Recommendation Systems Transform E‑Commerce with LLMs

Tencent Advertising Technology

Dec 27, 2024 · Artificial Intelligence

Tencent's AutoML Research for Advertising Recommendation Systems

This article outlines Tencent's AutoML research, presenting several recent papers that introduce novel neural architecture search, feature selection, pooling, embedding size, and hyper‑parameter optimization techniques to improve the efficiency, accuracy, and scalability of large‑scale advertising recommendation systems.

AutoMLEmbedding Size SearchNeural Architecture Search

0 likes · 10 min read

Tencent's AutoML Research for Advertising Recommendation Systems

Baobao Algorithm Notes

Dec 18, 2024 · Artificial Intelligence

How STAR Enables Training‑Free Recommendations with Large Language Models

The article reviews the STAR framework, a training‑free recommendation approach that leverages large language model embeddings and collaborative co‑occurrence scores to retrieve and rank items, and evaluates its performance, hyper‑parameter effects, and ablation studies against existing LLM‑based recommender methods.

Artificial IntelligenceCollaborative FilteringLLM

0 likes · 10 min read

How STAR Enables Training‑Free Recommendations with Large Language Models

Kuaishou Tech

Nov 30, 2024 · Artificial Intelligence

Kuaishou and Tsinghua University Win First Prize in Qian Weichang Chinese Information Processing Award for Content Recommendation Technology

Kuaishou and Tsinghua University were honored with the first‑place Qian Weichang Chinese Information Processing Science and Technology Award for their collaborative content recommendation project, which achieved international‑level innovations in explainable recommendation, bias correction, and edge intelligence, and has been applied widely in Kuaishou's platform and top academic conferences.

Artificial IntelligenceExplainabilityKuaishou

0 likes · 5 min read

Kuaishou and Tsinghua University Win First Prize in Qian Weichang Chinese Information Processing Award for Content Recommendation Technology

Baobao Algorithm Notes

Nov 25, 2024 · Artificial Intelligence

How Non‑Autoregressive Generative Models Transform Recommendation Reranking

This article presents a KDD‑2024 accepted solution that replaces autoregressive generators with a non‑autoregressive model for video recommendation reranking, detailing the challenges, model architecture, novel loss function, extensive offline and online experiments, and practical Q&A from the conference.

KDD2024Recommendation SystemsReranking

0 likes · 11 min read

How Non‑Autoregressive Generative Models Transform Recommendation Reranking

NewBeeNLP

Nov 14, 2024 · Artificial Intelligence

What’s Trending in Recommendation Systems at KDD 2024? A Comprehensive Paper Overview

The 30th SIGKDD conference in Barcelona featured 2,046 research papers with a 20% acceptance rate, and this article compiles the 59 recommendation‑system papers—covering large‑model recommenders, graph‑based methods, sequential models, fairness, privacy, advertising, debiasing, reinforcement learning and more—for researchers to explore the latest academic advances.

KDD2024Large Language ModelsOnline Advertising

0 likes · 15 min read

What’s Trending in Recommendation Systems at KDD 2024? A Comprehensive Paper Overview

DataFunTalk

Nov 7, 2024 · Product Management

Strategy Product Definition, AI‑Era Trends, and Career Path in Recommendation Systems

This article introduces the concept and capability model of strategy products, outlines the three generations of product managers, presents a simplified talent development framework, discusses practical workflow, examines six AI‑era strategic product questions, and shares 2024 observations on recommendation performance and future skill development.

Artificial IntelligenceRecommendation Systemscareer development

0 likes · 13 min read

Strategy Product Definition, AI‑Era Trends, and Career Path in Recommendation Systems

Zhuanzhuan Tech

Nov 6, 2024 · Artificial Intelligence

Multi-Task Learning for E-commerce Search: Overview, Practices, and Model Design in the Zhuanzhuan Scenario

This article reviews the necessity, benefits, and practical implementations of multi-task learning in e‑commerce search, detailing model selection, architecture extensions such as ESMM and ESM², and future directions for handling user behavior sequences and multi‑objective optimization.

ESMMRecommendation Systemsconversion rate prediction

0 likes · 13 min read

Multi-Task Learning for E-commerce Search: Overview, Practices, and Model Design in the Zhuanzhuan Scenario

Baobao Algorithm Notes

Oct 24, 2024 · Artificial Intelligence

How NoteLLM-2 Boosts Multimodal Recommendations with In-Content Learning

NoteLLM-2 introduces multimodal In-Content Learning and Late Fusion to overcome visual‑modality bias in end‑to‑end fine‑tuned large representation models, delivering significant gains over baseline multimodal LLMs and traditional retrieval methods in recommendation tasks.

AI researchMultimodal LLMRecommendation Systems

0 likes · 11 min read

How NoteLLM-2 Boosts Multimodal Recommendations with In-Content Learning

Tencent Advertising Technology

Oct 14, 2024 · Artificial Intelligence

Generative Retrieval Based on Yuan Large Model: Implementation and Practice in Tencent Advertising

This paper presents the implementation and practice of generative retrieval based on Yuan large model in Tencent Advertising, addressing three key challenges: user intent capture, model alignment in advertising domain, and high-performance platform design under ROI constraints.

Large Language ModelsModel OptimizationPrompt Engineering

0 likes · 17 min read

Generative Retrieval Based on Yuan Large Model: Implementation and Practice in Tencent Advertising

DataFunSummit

Oct 5, 2024 · Artificial Intelligence

Optimizing TorchRec for Large‑Scale Recommendation Systems on PyTorch

This article details the performance‑focused optimizations applied to TorchRec, PyTorch's large‑scale recommendation system library, including CUDA graph capture, multithreaded kernel launches, pinned memory copies, and input‑distribution refinements that together achieve a 2.25× speedup on MLPerf DLRM‑DCNv2 across 16 DGX H100 nodes.

CUDA GraphDistributed TrainingGPU Optimization

0 likes · 11 min read

Optimizing TorchRec for Large‑Scale Recommendation Systems on PyTorch

NewBeeNLP

Sep 9, 2024 · Artificial Intelligence

Can Real‑Time Learning at Serving Time Transform Recommendation Re‑ranking?

This article introduces LAST, a novel online learning approach that updates recommendation models instantly at serving time, addressing real‑time learning challenges, re‑ranking complexities, and demonstrating superior offline and online performance in industrial e‑commerce scenarios.

AILASTRecommendation Systems

0 likes · 12 min read

Can Real‑Time Learning at Serving Time Transform Recommendation Re‑ranking?

JD Retail Technology

Aug 30, 2024 · Artificial Intelligence

GPU Optimization Practices for Training and Inference in JD Advertising Recommendation Systems

The article details JD Advertising's technical challenges and solutions for large‑scale sparse recommendation models, describing GPU‑focused storage, compute and I/O optimizations for both training and low‑latency inference, including distributed pipelines, heterogeneous deployment, batch aggregation, multi‑stream execution, and compiler extensions.

GPU OptimizationRecommendation SystemsTensorFlow

0 likes · 13 min read

GPU Optimization Practices for Training and Inference in JD Advertising Recommendation Systems

Alibaba Cloud Big Data AI Platform

Aug 22, 2024 · Artificial Intelligence

How RECom Accelerates Recommendation Model Inference on GPUs

The RECom compiler introduces a subgraph‑parallel fusion technique and symbolic shape handling to dramatically speed up GPU inference of deep recommendation models with massive embedding columns, achieving up to 6.61× lower latency and 1.91× higher throughput than TensorFlow baselines, while eliminating redundant computations.

CompilerGPU OptimizationRecommendation Systems

0 likes · 10 min read

How RECom Accelerates Recommendation Model Inference on GPUs

Model Perspective

Aug 18, 2024 · Fundamentals

How to Judge a Mathematical Model: 6 Practical Criteria for Success

This article outlines six essential criteria—accuracy, robustness, simplicity, explainability, generalization, and scalability—for evaluating the quality of mathematical models such as e‑commerce recommendation systems, helping readers assess whether a model is truly reliable or merely a flashy façade.

ExplainabilityModel EvaluationRecommendation Systems

0 likes · 3 min read

How to Judge a Mathematical Model: 6 Practical Criteria for Success

DataFunSummit

Aug 10, 2024 · Artificial Intelligence

Leveraging Large Language Models for Graph Recommendation System Optimization

This article reviews cutting‑edge research on integrating large language models with graph‑based recommendation systems, detailing four key strategies—LLM node embeddings, deep graph‑LLM fusion, model‑driven graph data training, and text‑modal enhancements—while analyzing representation learning, InfoNCE optimization, explainable recommendations, and extensive experimental validation.

ExplainabilityInfoNCELLM

0 likes · 18 min read

Leveraging Large Language Models for Graph Recommendation System Optimization

DataFunTalk

Aug 5, 2024 · Artificial Intelligence

Enhancing Taobao Display Advertising with Multimodal Representations: Challenges, Approaches, and Insights

This article presents a comprehensive study on integrating multimodal image‑text representations into large‑scale e‑commerce advertising CTR models, introducing a semantic‑aware contrastive pre‑training (SCL) method and two application algorithms (SimTier and MAKE) that together achieve over 1 % GAUC improvement and significant online gains.

CTR predictionRecommendation Systemscontrastive learning

0 likes · 21 min read

Enhancing Taobao Display Advertising with Multimodal Representations: Challenges, Approaches, and Insights

DataFunSummit

Aug 4, 2024 · Artificial Intelligence

Graph Technology Overview and Applications – From GraphGPT to Graph Databases

This article presents a comprehensive overview of recent advances in graph technology, covering GraphGPT for large language models, knowledge transfer on complex graphs, financial fraud detection, telecom network optimization, graph foundation models, Baidu's multi‑domain recommendation, high‑availability graph databases, and Kuaishou's efficient recommendation architecture.

Graph DatabasesLarge Language ModelsRecommendation Systems

0 likes · 4 min read

Graph Technology Overview and Applications – From GraphGPT to Graph Databases

Alimama Tech

Aug 2, 2024 · Artificial Intelligence

Multimodal Representations Boost Taobao Display Advertising CTR

Alibaba’s advertising team introduces semantic‑aware contrastive learning to pre‑train multimodal image‑text embeddings, integrates them via SimTier and MAKE into ID‑based CTR models, achieving up to 6.9% lift in Taobao display ad click‑through rates and improving long‑tail item performance.

CTR predictionRecommendation Systemscontrastive learning

0 likes · 21 min read

Multimodal Representations Boost Taobao Display Advertising CTR

DataFunSummit

Jul 29, 2024 · Artificial Intelligence

Large Language Models for Recommendation Systems: Current Progress, Challenges, and Future Directions

This article reviews the state‑of‑the‑art applications of large language models in recommendation systems, summarizing background knowledge, recent advances such as LLM4Rec, various tuning strategies, agent‑based approaches, open research problems, and future directions for generative recommendation.

AILLMModel tuning

0 likes · 24 min read

Large Language Models for Recommendation Systems: Current Progress, Challenges, and Future Directions

Meituan Technology Team

Jul 25, 2024 · Artificial Intelligence

Selected Meituan Papers Accepted at KDD 2024: Summaries of Five Long Papers

Meituan’s five long papers accepted at KDD 2024 introduce a dual‑intent model for search‑recommendation, a joint auction mechanism for ads, a robust ATE estimator for heavy‑tailed metrics, a decision‑focused causal learning framework for marketing, and an efficient on‑demand order‑pooling system for real‑time courier assignments.

Controlled ExperimentsKDD 2024Online Advertising

0 likes · 12 min read

Selected Meituan Papers Accepted at KDD 2024: Summaries of Five Long Papers

NewBeeNLP

Jul 22, 2024 · Artificial Intelligence

How Meta Scales User Modeling for Ads: Inside the SUM Framework

This article examines Meta's SUM (Scaling User Modeling) system, detailing its upstream‑downstream architecture, the SOAP online asynchronous serving platform, production optimizations, and extensive offline and online experiments that demonstrate significant gains in ad personalization performance.

MetaRecommendation SystemsUser Modeling

0 likes · 19 min read

How Meta Scales User Modeling for Ads: Inside the SUM Framework

DataFunSummit

Jul 10, 2024 · Artificial Intelligence

Applying Large Language Models to Recommendation Systems at Ant Group

The article presents Ant Group's research on integrating large language models into recommendation pipelines, covering background challenges, knowledge extraction, teacher‑model distillation, efficient deployment, experimental results, and future directions to improve accuracy and reduce bias.

AILLMRecommendation Systems

0 likes · 13 min read

Applying Large Language Models to Recommendation Systems at Ant Group

NewBeeNLP

Jul 5, 2024 · Artificial Intelligence

Unveiling Meta’s Wukong: How Scaling Laws Boost Large‑Scale Recommendation Performance

Meta’s new paper introduces the Wukong model, demonstrating that expanding dense‑layer parameters and computational FLOPs in large‑scale recommendation systems follows a clear scaling law, yielding consistent performance gains across massive internal datasets, with detailed analysis of feature modules, parameter impacts, and experimental results.

CTR modelsMetaRecommendation Systems

0 likes · 10 min read

Unveiling Meta’s Wukong: How Scaling Laws Boost Large‑Scale Recommendation Performance

Xiaohongshu Tech REDtech

Jun 20, 2024 · Artificial Intelligence

Xiaohongshu 2024 Large Model Frontier Paper Sharing Live Event

On June 27, 2024, Xiaohongshu’s technical team will livestream a two‑hour session across WeChat Channels, Bilibili, Douyin and Xiaohongshu, showcasing six top‑conference papers on large‑model advances—including early‑stopping and fine‑grained self‑consistency, novel evaluation methods, negative‑sample‑assisted distillation, and LLM‑based note recommendation—followed by a Q&A and recruitment briefing.

AI researchLarge Language ModelsModel Evaluation

0 likes · 12 min read

Xiaohongshu 2024 Large Model Frontier Paper Sharing Live Event

DataFunSummit

Jun 17, 2024 · Artificial Intelligence

Strategies for Reducing Cost and Improving Efficiency in Recommendation Systems with Alibaba Cloud PAI‑Rec

This article discusses how Alibaba Cloud’s AI platform PAI‑Rec reduces recommendation system costs and boosts efficiency by optimizing training resources, leveraging FeatureStore, EasyRec and TorchEasyRec frameworks, detailing workflow stages, feature consistency, GPU acceleration, componentized model configuration, and practical deployment timelines.

AI platformFeature StoreGPU Acceleration

0 likes · 14 min read

Strategies for Reducing Cost and Improving Efficiency in Recommendation Systems with Alibaba Cloud PAI‑Rec

DataFunSummit

Jun 16, 2024 · Artificial Intelligence

Reinforcement Learning in Recommendation Systems: Practice, Challenges, and Industry Advances

This article presents a comprehensive overview of applying reinforcement learning to recommendation systems, covering background challenges, practical exploration, frontier research directions, multi‑agent and inverse RL approaches, evaluation methods, and future outlooks, based on a KDD‑published study and industry experience.

Inverse RLOffline RLRecommendation Systems

0 likes · 24 min read

Reinforcement Learning in Recommendation Systems: Practice, Challenges, and Industry Advances

DataFunTalk

Jun 15, 2024 · Artificial Intelligence

DataFunSummit2024 Recommendation System Architecture Summit Overview

The DataFunSummit2024 Recommendation System Architecture Summit invites participants to explore cutting‑edge advances in large‑model recommendation, training and inference optimization, feature engineering, multi‑task modeling, and graph‑based techniques through a series of expert talks and panel discussions from leading industry and academic researchers.

AIRecommendation Systemsconference

0 likes · 33 min read

DataFunSummit2024 Recommendation System Architecture Summit Overview

JD Cloud Developers

Jun 13, 2024 · Artificial Intelligence

How LLMs Are Redefining Recommender Systems for JD Union Ads

This article surveys the impact of large language models on recommendation systems, outlines generative recommender architectures, discusses challenges of JD Union advertising, presents a semantic‑ID based solution with training and inference details, and reports offline and online experimental results.

AILLMRecommendation Systems

0 likes · 22 min read

How LLMs Are Redefining Recommender Systems for JD Union Ads

DataFunSummit

Jun 12, 2024 · Artificial Intelligence

Large Language Model (LLM) Powered Recommendation Systems: Overview, Techniques, Challenges, and Future Directions

This article reviews how large language models are transforming recommendation systems, covering their fundamentals, recent LLM‑enabled methods for representation, learning and generalization, challenges such as scalability, bias and privacy, and future research directions including personalized prompts and robust model integration.

LLMRecommendation Systemsmodel generalization

0 likes · 19 min read

Large Language Model (LLM) Powered Recommendation Systems: Overview, Techniques, Challenges, and Future Directions

NewBeeNLP

May 24, 2024 · Artificial Intelligence

How NoteLLM Boosts Cold‑Start Recommendation with Generative Contrastive Learning

This article reviews the NoteLLM paper, which leverages Llama 2 to create richer text embeddings and automatically generate tags and categories for note recommendation, addressing cold‑start issues through a multitask prompt design, generative‑contrastive learning, and collaborative supervised fine‑tuning, and demonstrates strong offline and online gains.

EmbeddingGenerative Contrastive LearningLLM

0 likes · 14 min read

How NoteLLM Boosts Cold‑Start Recommendation with Generative Contrastive Learning

DataFunTalk

May 9, 2024 · Artificial Intelligence

Graph Model Practices and Applications in Baidu Recommendation System

This article introduces the background of graph data, explains common graph modeling algorithms such as graph embedding and graph neural networks, compares their strengths, and details the evolution and large‑scale deployment of Feed graph models in Baidu's recommendation platform.

BaiduEmbeddingRecommendation Systems

0 likes · 11 min read

Graph Model Practices and Applications in Baidu Recommendation System

DataFunSummit

May 4, 2024 · Artificial Intelligence

Applications of Large Language Models in Recommendation Systems: Overview and Future Directions

This article provides a comprehensive overview of how large language models (LLMs) are integrated into recommendation systems, detailing two main paradigms—LLM as a component and LLM as a standalone system—while discussing their impact on retrieval, ranking, prompting, and outlining future research challenges such as multimodal recommendation, hallucination mitigation, bias reduction, and agent‑based approaches.

AIFuture DirectionsLLM

0 likes · 6 min read

Applications of Large Language Models in Recommendation Systems: Overview and Future Directions

JD Tech Talk

Apr 25, 2024 · Artificial Intelligence

Evolution of JD Recommendation Advertising Ranking and Auction Mechanisms

This article reviews the evolution of JD’s recommendation advertising ranking mechanism, covering its economic auction origins, challenges of multi‑material valuation, user interest uncertainty, and multi‑item auction fairness, and describes AI‑driven solutions such as deep auction models and reinforcement‑learning‑based ListVCG.

RankingRecommendation Systemsauction

0 likes · 19 min read

Evolution of JD Recommendation Advertising Ranking and Auction Mechanisms

AntTech

Apr 17, 2024 · Artificial Intelligence

LLMRG: Improving Recommendations through Large Language Model Reasoning Graphs

LLMRG introduces a novel framework that leverages large language models to construct personalized reasoning graphs, integrating chain reasoning, self‑verification, divergent extension, and knowledge‑base self‑improvement, thereby enhancing recommendation accuracy, interpretability, and performance across multiple benchmark datasets without additional user or item information.

AILarge Language ModelsRecommendation Systems

0 likes · 9 min read

LLMRG: Improving Recommendations through Large Language Model Reasoning Graphs

DataFunTalk

Apr 3, 2024 · Artificial Intelligence

Future Directions of Recommendation Systems: Retention, User Growth, Content Ecosystem, Multi‑Objective Optimization, and Large‑Model Fusion

This presentation outlines the current bottlenecks of conventional recommendation pipelines and proposes a 2026 roadmap that includes retention improvement, user‑growth strategies, content‑ecosystem metrics, Pareto‑optimal multi‑objective optimization, long‑term value modeling, site‑wide spatial optimization, interactive recommendation, personalized modeling, and the integration of large‑model fusion through the OneRec framework.

Large Language ModelsRecommendation SystemsUser Retention

0 likes · 18 min read

Future Directions of Recommendation Systems: Retention, User Growth, Content Ecosystem, Multi‑Objective Optimization, and Large‑Model Fusion

DataFunTalk

Apr 2, 2024 · Artificial Intelligence

User Portrait Algorithms: From Ontology‑Based Methods to Deep Learning and Future Directions

This article provides a comprehensive overview of user portrait algorithms, covering their historical development, ontology‑based traditional approaches, deep‑learning enhancements, representation‑learning techniques such as lookalike, active‑learning driven iteration, and the integration of large‑model world knowledge, while also discussing current challenges and future research directions.

Active LearningLarge Language ModelsOntology

0 likes · 26 min read

User Portrait Algorithms: From Ontology‑Based Methods to Deep Learning and Future Directions

DataFunSummit

Mar 29, 2024 · Artificial Intelligence

Large Language Model (LLM) Revolution in Recommendation Systems: Overview, Techniques, and Future Directions

This article reviews how the rapid rise of large language models, exemplified by ChatGPT, is transforming recommendation systems by addressing traditional ID‑centric limitations, introducing prompt‑based and ID‑free representations, discussing recent research advances, practical challenges, and future research directions.

AILLMRecommendation Systems

0 likes · 18 min read

Large Language Model (LLM) Revolution in Recommendation Systems: Overview, Techniques, and Future Directions

DataFunTalk

Mar 28, 2024 · Artificial Intelligence

Multi-Task and Multi-Scenario Algorithms for Recommendation Systems: Methods, Challenges, and Applications

This article presents a comprehensive overview of multi‑task and multi‑scenario recommendation algorithms, detailing background challenges, algorithm classifications such as TAML, CausalInt, and DFFM, their modular designs, experimental validations, and practical Q&A insights for large‑scale advertising systems.

Recommendation Systemsadvertising algorithmsmachine learning

0 likes · 19 min read

Multi-Task and Multi-Scenario Algorithms for Recommendation Systems: Methods, Challenges, and Applications

NewBeeNLP

Mar 28, 2024 · Industry Insights

How Meta’s HSTU Architecture Scales Recommendation Systems Beyond Decades of Deep Models

Meta introduces a generative recommendation framework (GR) built on the Hierarchical Sequential Transduction Unit (HSTU) that unifies heterogeneous features, treats user behavior as a new modality, and leverages novel encoder and inference optimizations to achieve order‑of‑magnitude scaling in model size, training compute, and online latency while delivering 12‑18% online gains over traditional deep recommendation models.

Generative ModelsHSTUMeta

0 likes · 36 min read

How Meta’s HSTU Architecture Scales Recommendation Systems Beyond Decades of Deep Models

NewBeeNLP

Mar 15, 2024 · Industry Insights

How Meta’s Generative Recommendation (GR) Is Redefining Feature Engineering

Meta’s new Generative Recommendation (GR) paper replaces a decade‑old hierarchical feature paradigm with an ultra‑long sequence transformer that directly fuses user profiles, behaviors, and targets, offering stronger feature crossing, richer information utilization, and massive compute gains, while revealing scaling‑law effects in recommendation systems.

Generative ModelsMetaRecommendation Systems

0 likes · 9 min read

How Meta’s Generative Recommendation (GR) Is Redefining Feature Engineering

Ops Development & AI Practice

Mar 13, 2024 · Artificial Intelligence

How Vector Retrieval Powers AI Model Training and Real-World Applications

Vector retrieval, based on converting data into high‑dimensional vectors and measuring similarity, enables fast, accurate search across massive datasets, supporting AI tasks such as search engines, recommendation, NLP, and computer vision, and plays a crucial role in large‑model training for data selection, anomaly detection, and model optimization.

AI trainingApproximate Nearest NeighborRecommendation Systems

0 likes · 6 min read

How Vector Retrieval Powers AI Model Training and Real-World Applications

DaTaobao Tech

Feb 19, 2024 · Artificial Intelligence

AI/ML Technology Articles Collection

This collection compiles technical articles that explore diverse AI/ML applications, from deploying large language models on MacBooks and building e‑commerce recommendation engines, to leveraging the LangChain framework, creating AIGC‑driven fashion solutions, and implementing Stable Diffusion for image generation.

AIAIGCLLM

0 likes · 1 min read

DataFunTalk

Feb 7, 2024 · Big Data

Kuaishou's Practices for Large‑Scale Model Data Processing, Real‑Time Feature Handling, and Storage

This article presents Kuaishou's end‑to‑end engineering solutions for handling massive, real‑time recommendation model data, covering scenario description, complex business pipelines, trillion‑parameter model storage, high‑throughput processing with Flink and NVM, and future directions for cloud‑native scalability.

KuaishouNVM storageRecommendation Systems

0 likes · 15 min read

Kuaishou's Practices for Large‑Scale Model Data Processing, Real‑Time Feature Handling, and Storage

DataFunSummit

Jan 23, 2024 · Artificial Intelligence

Meta-Learning and Cross-Domain Recommendation: Industrial Practices at Tencent TRS

This article presents Tencent TRS's industrial practice of applying meta‑learning and cross‑domain recommendation to address personalization challenges, detailing problem definitions, solution architectures, algorithmic choices such as MAML, deployment strategies, and the cost‑effective outcomes achieved across multiple scenarios.

MAMLRecommendation Systemscross-domain

0 likes · 16 min read

Meta-Learning and Cross-Domain Recommendation: Industrial Practices at Tencent TRS

Kuaishou Tech

Jan 23, 2024 · Artificial Intelligence

Highlights of Five Selected AAAI 2024 Papers on Recommendation, Retrieval, and Video Generation

This article presents concise overviews of five AAAI 2024 accepted papers covering multi‑stage reinforcement‑learning recommendation, error‑adaptive watch‑time prediction, coarse‑to‑fine text‑to‑video retrieval, enhanced fashion image retrieval, and conditional image‑to‑video generation, each with authors, download links, and reported performance gains.

AAAI 2024Artificial IntelligenceRecommendation Systems

0 likes · 14 min read

Highlights of Five Selected AAAI 2024 Papers on Recommendation, Retrieval, and Video Generation

Model Perspective

Jan 22, 2024 · Artificial Intelligence

How A/B Testing and the ε‑Greedy Multi‑Armed Bandit Can Boost Decisions

This article explains the principles of A/B testing and the ε‑greedy multi‑armed bandit algorithm, illustrates their practical use in e‑commerce recommendation optimization, and draws broader life lessons about balancing exploration and exploitation for better personal and professional decisions.

A/B testingRecommendation Systemsexploration vs exploitation

0 likes · 6 min read

How A/B Testing and the ε‑Greedy Multi‑Armed Bandit Can Boost Decisions

DataFunTalk

Jan 19, 2024 · Artificial Intelligence

Improving the MIND Multi‑Interest Recommendation Model with Capsule Initialization and Routing Enhancements

This article presents a comprehensive study of the MIND multi‑interest recommendation model, detailing its original architecture, identified shortcomings, and proposed enhancements—including capsule initialization via max‑min and Markov methods, routing simplifications, and training adjustments—along with experimental results and business impact assessments.

AIMINDRecommendation Systems

0 likes · 19 min read

Improving the MIND Multi‑Interest Recommendation Model with Capsule Initialization and Routing Enhancements

Sohu Tech Products

Jan 10, 2024 · Artificial Intelligence

Baidu's Practices and Insights on Recommendation Ranking

Baidu’s recommendation ranking system handles billions of daily impressions and millions of users by combining discrete and cross features, bias mitigation, and long‑short sequence modeling within a multi‑stage funnel and hierarchical architecture, while planning to integrate large language models for generative, interpretable, and decision‑oriented recommendations.

AIBaiduRecommendation Systems

0 likes · 19 min read

Baidu's Practices and Insights on Recommendation Ranking

DataFunTalk

Jan 7, 2024 · Artificial Intelligence

Baidu's Recommendation Ranking: Background, Feature Design, Algorithms, Architecture, and Future Directions

This article presents Baidu's comprehensive approach to feed recommendation ranking, covering business and data background, feature engineering principles, core algorithmic strategies, system architecture design, and upcoming plans to integrate large language models for more intelligent and fair recommendations.

BaiduRecommendation Systemsfeature engineering

0 likes · 19 min read

Baidu's Recommendation Ranking: Background, Feature Design, Algorithms, Architecture, and Future Directions

DataFunTalk

Jan 6, 2024 · Artificial Intelligence

Causal Debiasing Techniques for Recommendation and Marketing Scenarios

This article presents Ant Group's causal debiasing techniques for recommendation and marketing, covering bias background, data‑fusion based MDI model, back‑door adjustment methods, experimental results on public and industry datasets, and practical applications in advertising and e‑commerce.

Recommendation Systemscausal inferencedata fusion

0 likes · 16 min read

Causal Debiasing Techniques for Recommendation and Marketing Scenarios

DataFunSummit

Dec 27, 2023 · Artificial Intelligence

Two-Stage Constrained Actor-Critic for Short‑Video Recommendation and a Reinforcement‑Learning Multi‑Task Framework

This article presents a two‑stage constrained actor‑critic (TSCAC) algorithm that models short‑video recommendation as a constrained reinforcement‑learning problem, details its theoretical formulation and optimization loss, and validates its superiority through extensive offline and online experiments, followed by a multi‑task reinforcement‑learning framework (RMTL) that further improves multi‑objective recommendation performance.

Recommendation Systemsconstrained optimizationmulti-task learning

0 likes · 16 min read

Two-Stage Constrained Actor-Critic for Short‑Video Recommendation and a Reinforcement‑Learning Multi‑Task Framework

DataFunSummit

Dec 22, 2023 · Artificial Intelligence

Cross‑Domain Multi‑Objective Estimation and Fusion in Baidu Video Recommendation: Design, Modeling, and System Evolution

This article shares Baidu's experience and thinking on cross‑domain multi‑objective estimation and fusion for video recommendation, covering background, system overview, multi‑objective design and modeling, long‑term value attribution, cross‑domain network architecture, and the evolution‑strategy based fusion approach.

BaiduRecommendation Systemscross-domain

0 likes · 13 min read

Cross‑Domain Multi‑Objective Estimation and Fusion in Baidu Video Recommendation: Design, Modeling, and System Evolution

Alibaba Cloud Developer

Dec 20, 2023 · Artificial Intelligence

Turning Complexity into Results: Practical Strategies for Recommendation Engineers

This article explores why recommendation engineers often struggle to deliver measurable outcomes, examining system complexity, uncertainty, delayed feedback, and personal belief, and then offers concrete principles and actionable approaches to prioritize work, align with business goals, and achieve tangible results.

AIAlgorithm EngineeringRecommendation Systems

0 likes · 11 min read

Turning Complexity into Results: Practical Strategies for Recommendation Engineers

Architect

Dec 14, 2023 · Artificial Intelligence

How Multi‑Task Multi‑Scene Modeling Powers ZhiZhuan’s Search: Algorithms, Industry Practices, and Lessons

This article analyzes the challenges of multi‑task and multi‑scene recommendation for large‑scale C‑end services, reviews key academic and industry solutions such as Shared‑Bottom, MMoE, PLE, ESMM, LHUC, PEPNet, MTMS and HiNet, and details ZhiZhuan’s end‑to‑end architecture that achieved over 6% click‑through and 2% conversion improvements.

AI model architectureIndustry Case StudyRecommendation Systems

0 likes · 15 min read

How Multi‑Task Multi‑Scene Modeling Powers ZhiZhuan’s Search: Algorithms, Industry Practices, and Lessons

DataFunTalk

Dec 12, 2023 · Artificial Intelligence

Challenges and Considerations of Recommendation Systems: Evaluation, Data Leakage, and the Role of Large Models

This article examines recommendation system problem definitions, differences between academia and industry, offline evaluation pitfalls and data leakage issues, data construction challenges with datasets like MovieLens, and evaluates whether large language models can serve as effective solutions for modern recommendation tasks.

Large Language ModelsRecommendation Systemsdata leakage

0 likes · 20 min read

Challenges and Considerations of Recommendation Systems: Evaluation, Data Leakage, and the Role of Large Models

Sohu Tech Products

Dec 6, 2023 · Artificial Intelligence

Real-time Controllable Multi-Objective Re-ranking Models for Taobao Feed Recommendation

The paper introduces a real‑time controllable, multi‑objective re‑ranking framework for Taobao’s feed recommendation that combines actor‑critic reinforcement learning with hypernetworks to instantly adjust objective weights, handling diverse media and cold‑start constraints while delivering higher click‑through, diversity, and cold‑start ratios with only 20‑25 ms latency.

AlibabaReal-time ControlRecommendation Systems

0 likes · 34 min read

Real-time Controllable Multi-Objective Re-ranking Models for Taobao Feed Recommendation

DataFunSummit

Dec 5, 2023 · Artificial Intelligence

Scenario-Adaptive and Self-Supervised Multi-Scenario Personalized Recommendation (SASS)

This article presents a comprehensive study of a scenario‑adaptive and self‑supervised multi‑scenario recommendation model (SASS) for Taobao, detailing its motivation, adaptive multi‑scenario architecture, two‑stage pre‑training and fine‑tuning, experimental validation, deployment in the recall stage, and practical challenges addressed through Q&A.

AlibabaRecommendation SystemsSelf-supervised Learning

0 likes · 36 min read

Scenario-Adaptive and Self-Supervised Multi-Scenario Personalized Recommendation (SASS)

DataFunSummit

Dec 2, 2023 · Artificial Intelligence

OPPO’s Unified Modeling Strategy for App Distribution: Balancing Cost Reduction and User Value

In this interview, OPPO’s senior manager Lai Hongke explains how the company tackles the challenges of sparse, cross‑scenario data in app distribution by deploying a unified modeling framework, MMOE sharing, and the oCPX capability to simultaneously cut costs, improve recommendation performance, and preserve user value across its software store and game center.

AIData EngineeringOPPO

0 likes · 11 min read

OPPO’s Unified Modeling Strategy for App Distribution: Balancing Cost Reduction and User Value

DataFunTalk

Dec 2, 2023 · Artificial Intelligence

OPPO's Unified Modeling for App Distribution: Balancing Cost Reduction and User Value

The article examines how OPPO tackles the challenges of sparse, multi‑scenario app‑distribution data by deploying a unified modeling framework, leveraging MMoe and oCPX techniques to enhance recommendation performance, reduce costs, and preserve user value across its software store and game center.

OPPORecommendation Systemsdata sparsity

0 likes · 11 min read

OPPO's Unified Modeling for App Distribution: Balancing Cost Reduction and User Value

DataFunTalk

Nov 29, 2023 · Artificial Intelligence

Cross-Domain Multi-Objective Estimation and Fusion in Baidu Video Recommendation

This article presents Baidu's technical experience on designing, estimating, and fusing cross-domain multi-objective models for its immersive video recommendation system, covering business background, system architecture, target design, long‑term value modeling, and evolution strategies.

AIRecommendation Systemscross-domain

0 likes · 14 min read

Cross-Domain Multi-Objective Estimation and Fusion in Baidu Video Recommendation

DataFunTalk

Nov 27, 2023 · Artificial Intelligence

STAN: A User‑Lifecycle‑Aware Multi‑Task Recommendation Model for Shopee

This article introduces STAN, a user‑lifecycle‑aware multi‑task recommendation model proposed by Shopee that refines CTR, CVR, and stay‑time predictions by identifying and tracking user states, demonstrates offline gains on Shopee and public datasets, and reports online improvements in click‑through, dwell‑time, and order metrics.

CTRCVRRecommendation Systems

0 likes · 8 min read

STAN: A User‑Lifecycle‑Aware Multi‑Task Recommendation Model for Shopee

360 Smart Cloud

Nov 20, 2023 · Artificial Intelligence

Overview of Recent Open‑Source AI Models and Tools (November 2023)

This article summarizes a collection of newly released open‑source AI projects covering natural‑language processing, multimodal processing, intelligent agents, recommendation systems, and model training acceleration, providing brief descriptions, key capabilities, and links to their repositories.

AILarge Language ModelsMultimodal

0 likes · 9 min read

Overview of Recent Open‑Source AI Models and Tools (November 2023)

Practical DevOps Architecture

Nov 20, 2023 · Backend Development

Comprehensive Python Full-Stack Development Course Outline (28 Chapters)

This article presents a detailed 28‑chapter curriculum for mastering Python full‑stack development, covering Linux basics, Python fundamentals, web front‑end design with Vue, RESTful API creation with Flask, Django and Django REST Framework, big‑data processing with Hadoop, Spark and MapReduce, feature engineering, recommendation systems, and live streaming system implementation.

Big DataPythonRecommendation Systems

0 likes · 3 min read

Comprehensive Python Full-Stack Development Course Outline (28 Chapters)

Alimama Tech

Nov 15, 2023 · Artificial Intelligence

Hybrid Contrastive Constraints for Multi-Scenario Ad Ranking (HC²)

The HC² framework enhances multi‑scenario ad ranking by jointly applying a generalized contrastive loss on shared representations and an individual contrastive loss on scenario‑specific layers, using label‑aware positive sampling, diffusion‑noise negative sampling, and inverse‑similarity weighting, achieving consistent offline gains and up to 2.5% CVR and 3.7% GMV improvements in Alibaba’s live system.

Recommendation Systemsad rankingcontrastive learning

0 likes · 16 min read

Hybrid Contrastive Constraints for Multi-Scenario Ad Ranking (HC²)

DataFunTalk

Nov 14, 2023 · Artificial Intelligence

Real-Time Controllable Multi-Objective Re‑ranking for Taobao Feed

This article presents a comprehensive study of a controllable multi‑objective re‑ranking model for Taobao's information‑flow recommendation, detailing the challenges of complex feed scenarios, three modeling paradigms (V1‑V3), an actor‑critic reinforcement learning framework with hypernet‑generated weights, and extensive online evaluation results.

Real-time ControlRecommendation SystemsRe‑ranking

0 likes · 31 min read

Real-Time Controllable Multi-Objective Re‑ranking for Taobao Feed

Sohu Tech Products

Nov 8, 2023 · Artificial Intelligence

Two‑Stage Constrained Actor‑Critic for Short‑Video Recommendation and a Reinforcement‑Learning Multi‑Task Recommendation Framework

The presentation introduces a two‑stage constrained actor‑critic algorithm that learns auxiliary policies for interaction signals before optimizing watch‑time under KL constraints, and a reinforcement‑learning multi‑task learning framework that models session‑level dynamics with adaptive multi‑critic weighting, both achieving significant offline and online gains in short‑video recommendation.

Recommendation Systemsactor-criticconstrained optimization

0 likes · 16 min read

Two‑Stage Constrained Actor‑Critic for Short‑Video Recommendation and a Reinforcement‑Learning Multi‑Task Recommendation Framework

Zhuanzhuan Tech

Nov 7, 2023 · Artificial Intelligence

Multi-Task Multi-Scenario Modeling: Challenges, Industry Solutions, and Zhaozhuan's Practice

This article outlines the challenges of multi-task and multi-scenario modeling for large-scale C-end services, reviews key industry approaches such as Shared-Bottom, MMoE, PLE, ESMM, and LHUC, and details Zhaozhuan’s own EPNET-based solution that improved click-through and conversion rates.

CTRCVRRecommendation Systems

0 likes · 13 min read

Multi-Task Multi-Scenario Modeling: Challenges, Industry Solutions, and Zhaozhuan's Practice

Xiaohongshu Tech REDtech

Nov 6, 2023 · Artificial Intelligence

Large Models and Recommendation Systems: Challenges, Opportunities, and Future Directions

At CNCC 2023, leading researchers and industry experts convened to examine how large language models can transform recommendation systems, outlining four core challenges—model integration, fluency versus intelligence, hallucination versus deception, and user understanding—while highlighting opportunities such as multimodal content, cold‑start solutions, zero‑shot ranking, instruction‑driven algorithms, and responsible, interactive recommendation pipelines.

AICNCC 2023LLM applications

0 likes · 16 min read

Large Models and Recommendation Systems: Challenges, Opportunities, and Future Directions

DataFunTalk

Nov 6, 2023 · Artificial Intelligence

Two‑Stage Constrained Actor‑Critic Reinforcement Learning for Short‑Video Recommendation and a Multi‑Task RL Framework

This article presents a two‑stage constrained actor‑critic reinforcement learning algorithm for short‑video recommendation, models the problem as a constrained MDP, details the algorithm’s stages, and reports extensive offline and online experiments showing superior watch‑time and interaction metrics, followed by a multi‑task RL framework and its evaluations.

Recommendation Systemsconstrained optimizationmulti‑task learning

0 likes · 16 min read

Two‑Stage Constrained Actor‑Critic Reinforcement Learning for Short‑Video Recommendation and a Multi‑Task RL Framework

DataFunTalk

Nov 3, 2023 · Product Management

Strategy Product Management: Principles, Frameworks, and Q&A for Content Recommendation

This article explains the role and mindset of a strategy product manager, outlines the decision‑making framework for content recommendation platforms, compares it with related positions, and answers practical questions about value, AI impact, commercial‑consumer trade‑offs, and content creation versus consumption.

AI impactData AnalysisRecommendation Systems

0 likes · 16 min read

Strategy Product Management: Principles, Frameworks, and Q&A for Content Recommendation

DataFunTalk

Oct 31, 2023 · Artificial Intelligence

Intelligent Growth Algorithms and Applications in the Smartphone Industry – OPPO Andes Smart Cloud

This article presents OPPO Andes Smart Cloud's intelligent growth algorithm framework for the smartphone sector, detailing industry background, data and model architecture, four real-world application cases—including AIGC content generation, multimodal recommendation, causal inference, and precise advertising—and summarizing key insights from a technical Q&A session.

AIGCRecommendation SystemsUplift Modeling

0 likes · 22 min read

Intelligent Growth Algorithms and Applications in the Smartphone Industry – OPPO Andes Smart Cloud

DataFunSummit

Oct 23, 2023 · Artificial Intelligence

Large Models in Recommendation Systems: Evaluation Challenges, Data Leakage, and Practical Considerations

This article examines how large language models fit into recommendation systems by discussing problem definitions, offline evaluation pitfalls such as data leakage, dataset construction issues exemplified by MovieLens, and the practical limits of using LLMs as a universal solution.

MovieLensRecommendation Systemsdata leakage

0 likes · 18 min read

Large Models in Recommendation Systems: Evaluation Challenges, Data Leakage, and Practical Considerations

DataFunSummit

Oct 14, 2023 · Artificial Intelligence

Career Planning for Algorithm Engineers: Stages, Strategies, and Skill Development

This article outlines the three key career stages for algorithm engineers, offers practical planning advice through vision, self‑evaluation and action, and discusses industry trends, skill‑building paths, mindset, and work‑life balance to help engineers navigate a volatile tech landscape.

AIAlgorithm EngineeringRecommendation Systems

0 likes · 29 min read

Career Planning for Algorithm Engineers: Stages, Strategies, and Skill Development

DataFunSummit

Oct 12, 2023 · Artificial Intelligence

Graph Models in Baidu's Recommendation System: Foundations, Algorithms, and Feed Model Evolution

This article explains how graph models are applied in Baidu's recommendation system, covering graph basics, common graph algorithms such as graph embeddings and GNNs, and the evolution and deployment of the Feed graph model across multiple product lines.

BaiduRecommendation Systemsgraph embeddings

0 likes · 10 min read

Graph Models in Baidu's Recommendation System: Foundations, Algorithms, and Feed Model Evolution

DataFunSummit

Oct 9, 2023 · Artificial Intelligence

Multi-Task and Multi-Scenario Algorithms for Recommendation Systems: Methods, Challenges, and Applications

This article presents a comprehensive overview of multi‑task and multi‑scenario algorithms applied to recommendation systems, covering background challenges, algorithm taxonomy, recent research, detailed model architectures such as TAML, CausalInt and DFFM, experimental results on public and private datasets, and a Q&A discussion.

AdvertisingRecommendation Systemsmachine learning

0 likes · 20 min read

DataFunSummit

Oct 5, 2023 · Artificial Intelligence

Fairness in Recommendation Systems: Consumer and Provider Perspectives

This article examines the fairness of recommendation systems from both consumer and provider viewpoints, discussing sources of bias, definitions of equality and equity, measurement metrics such as CGF and MMF, and proposes causal embedding models to mitigate unfairness while ensuring sustainable system performance.

Recommendation Systemscausal inferenceconsumer perspective

0 likes · 9 min read

Fairness in Recommendation Systems: Consumer and Provider Perspectives

DataFunSummit

Oct 4, 2023 · Artificial Intelligence

Comprehensive Overview of Recommendation System Technologies and Their Evolution

This article provides a detailed overview of modern recommendation system technology, covering system architecture, user understanding layers, various recall and ranking techniques, additional algorithmic directions such as cold‑start and bias modeling, and the evolving evaluation metrics used in practice.

Recommendation SystemsUser Modelingbias modeling

0 likes · 14 min read

Comprehensive Overview of Recommendation System Technologies and Their Evolution

DataFunTalk

Oct 1, 2023 · Artificial Intelligence

Research and Product Applications of Causal Inference for Solving Recommendation System Bias

In this talk, senior researcher Dai Quanyu from Huawei Noah's Ark Lab presents his work on applying causal inference to identify and correct various biases in recommendation systems, detailing underlying theoretical frameworks, bias‑mitigation algorithms such as inverse propensity weighting and robust learning, and real‑world product deployments.

AIBias MitigationRecommendation Systems

0 likes · 3 min read

Research and Product Applications of Causal Inference for Solving Recommendation System Bias

Alimama Tech

Sep 20, 2023 · Artificial Intelligence

CCF C³ Forum: AI Technology Driving Business Transformation

The 23rd CCF C³ Forum, organized by Alibaba’s Alimama and the CCF CTO Club, examined how large‑model AI is reshaping intelligent business technology, from data‑driven to knowledge‑driven approaches, enhancing e‑commerce with smarter search, personalized recommendations, content creation, and guiding merchants on future AI‑native strategies.

AI technologyAI-native businessData Intelligence

0 likes · 8 min read

CCF C³ Forum: AI Technology Driving Business Transformation

DaTaobao Tech

Sep 13, 2023 · Artificial Intelligence

Integrating Large Language Models with Recommendation Systems: Paradigms, Methods, and Experiments

The article surveys how large language models can be integrated into recommendation systems, either as feature extractors or as end‑to‑end recommenders, showing that LLM‑derived semantics improve recall, ranking, diversity, and user experience, and outlining future multimodal, efficiency, and re‑ranking directions.

EmbeddingLLMMultimodal

0 likes · 19 min read

Integrating Large Language Models with Recommendation Systems: Paradigms, Methods, and Experiments

DataFunSummit

Sep 1, 2023 · Artificial Intelligence

Observational Causal Inference and De‑Confounding Techniques for Industrial Applications

This article introduces the fundamentals of causal inference from observational data, explains confounding and the SUTVA assumptions, presents the do‑operator, and details four de‑confounding strategies—including RCT‑based resampling, feature‑decomposition, double machine learning, and back‑/front‑door adjustments—followed by real‑world applications in recommendation systems and resource allocation.

Recommendation Systemscausal inferencedeconfounding

0 likes · 22 min read

Observational Causal Inference and De‑Confounding Techniques for Industrial Applications

Xiaohongshu Tech REDtech

Aug 25, 2023 · Artificial Intelligence

DataFunSummit 2023: Recommendation Systems Online Summit

The DataFunSummit 2023 online summit (August 26‑27) will explore eight recommendation‑system topics—including core and engineering architecture, model training/inference, large models, graphs, cold start, and multi‑task scenarios—featuring Xiaohongshu leaders who will present on graph‑based business architecture, integrated training‑inference pipelines, and user/content cold‑start strategies.

AI engineeringRecommendation Systemsarchitecture

0 likes · 6 min read

DataFunSummit 2023: Recommendation Systems Online Summit

NetEase LeiHuo UX Big Data Technology

Aug 23, 2023 · Artificial Intelligence

Model-Based Collaborative Filtering Algorithms for Game Item Recommendation

This article explains the principles of collaborative filtering, outlines its three main types—user‑based, item‑based, and model‑based—and focuses on model‑based approaches such as matrix factorization, clustering, and deep‑learning techniques for recommending personalized game items to improve player experience and monetization.

Artificial IntelligenceCollaborative FilteringModel-Based

0 likes · 7 min read

Model-Based Collaborative Filtering Algorithms for Game Item Recommendation

Ele.me Technology

Aug 22, 2023 · Artificial Intelligence

Multi-Granularity Attention Model for Group Recommendation (MGAM)

The Multi‑Granularity Attention Model (MGAM) improves group recommendation by extracting subset, group, and superset preferences through hierarchical attention and graph neural networks, fusing them via self‑attention, and achieves state‑of‑the‑art offline results and a 1.2% online CTR lift in Alibaba’s local‑life services.

AIRecommendation Systemsattention model

0 likes · 18 min read

Multi-Granularity Attention Model for Group Recommendation (MGAM)

DataFunTalk

Aug 21, 2023 · Artificial Intelligence

Can We Build Large-Scale Models for Recommendation Systems?

In this talk, Zhang Pengtao, a Sina Weibo technical expert with a Ph.D. in computer applications, explores how the strong memory capabilities of NLP large language models inspire the design of independent memory mechanisms for recommendation systems, covering model concepts, HCNet & MemoNet, experimental results, and practical takeaways for enhancing recommendation model performance.

AILarge Language ModelsMemory Mechanisms

0 likes · 2 min read

Can We Build Large-Scale Models for Recommendation Systems?

Ele.me Technology

Aug 17, 2023 · Artificial Intelligence

BASM: A Bottom‑up Adaptive Spatiotemporal Model for Online Food Ordering Service

BASM is a bottom‑up adaptive spatiotemporal model for online food ordering that uses hierarchical embedding, semantic transformation, and adaptive bias layers to dynamically modulate parameters according to time and location, thereby capturing multiple data distributions and achieving superior offline metrics and online A/B test performance.

CTR predictionRecommendation SystemsSpatiotemporal Modeling

0 likes · 18 min read

BASM: A Bottom‑up Adaptive Spatiotemporal Model for Online Food Ordering Service

Ele.me Technology

Aug 16, 2023 · Artificial Intelligence

Spatiotemporal-Enhanced Network for Click-Through Rate Prediction in Location‑Based Services

The paper introduces StEN, a spatiotemporal-enhanced network for CTR prediction in location-based services, combining static spatiotemporal feature activation, dynamic preference activation, and target attention, achieving state-of-the-art offline results and a 1.6% CTR lift in online tests.

Recommendation SystemsSpatiotemporal Modelingclick-through rate

0 likes · 19 min read

Spatiotemporal-Enhanced Network for Click-Through Rate Prediction in Location‑Based Services

Meituan Technology Team

Aug 10, 2023 · Artificial Intelligence

Selected Meituan Technical Papers from KDD 2023: Summaries of Seven Research Works

The article showcases seven Meituan research papers accepted at KDD 2023—spanning feed‑stream, cross‑domain, takeaway, bonus allocation, contour‑based segmentation, living‑needs prediction, and multilingual recommendation—detailing their novel methods, real‑world deployments, and concluding with an invitation for academic collaboration.

Artificial IntelligenceKDD 2023Meituan

0 likes · 17 min read

Selected Meituan Technical Papers from KDD 2023: Summaries of Seven Research Works

DataFunTalk

Aug 7, 2023 · Artificial Intelligence

DataFun Decision Intelligence Summit – Reinforcement Learning Forum Overview

The DataFun Decision Intelligence Summit brings together leading researchers and industry experts to present cutting‑edge reinforcement learning algorithms, safety considerations, distributional methods, and real‑world applications such as vehicle routing, recommender systems, and power‑grid scheduling, highlighting future directions and audience takeaways.

AIRecommendation Systemsdistributional RL

0 likes · 12 min read

DataFun Decision Intelligence Summit – Reinforcement Learning Forum Overview

DataFunSummit

Aug 5, 2023 · Big Data

Manbang Group's Real-Time Computing, Data Architecture, and Product Practices

Manbang Group shares its practical experiences and insights on real-time computing, multi‑cloud platform architecture, data warehousing with Flink and Holo, real‑time decision and feature platforms, and future plans for scaling these systems to support logistics and recommendation algorithms.

Cloud NativeFlinkHolo

0 likes · 16 min read

Manbang Group's Real-Time Computing, Data Architecture, and Product Practices

Bitu Technology

Aug 2, 2023 · Artificial Intelligence

Tubi's Recall Exploration: Embedding‑Based Candidate Generation for Scalable Video Recommendations

This article details Tubi's multi‑stage recommendation system, focusing on the recall phase and describing how popularity metrics, embedding averaging, per‑video nearest‑neighbors, hierarchical clustering, real‑time ranking, and context‑aware sampling are combined to efficiently generate personalized video candidates at scale.

EmbeddingRecommendation Systemsmachine learning

0 likes · 10 min read

Tubi's Recall Exploration: Embedding‑Based Candidate Generation for Scalable Video Recommendations