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Recommendation systems

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AntTech
AntTech
May 15, 2025 · Artificial Intelligence

Live Deep Dive into Two Award‑Winning WSDM 2025 Papers on Popularity Bias in Recommendation Models and Graph‑Based Causal Inference

This announcement introduces a live session that will dissect two best‑paper award research works from WSDM 2025—one revealing how recommendation models amplify popularity bias through spectral analysis and proposing a lightweight regularizer, and the other presenting a graph disentangle causal model that integrates GNNs with structural causal models to improve causal inference on networked observational data.

Graph Neural NetworksRecommendation systemsWSDM 2025
0 likes · 4 min read
Live Deep Dive into Two Award‑Winning WSDM 2025 Papers on Popularity Bias in Recommendation Models and Graph‑Based Causal Inference
JD Tech
JD Tech
May 6, 2025 · Artificial Intelligence

One4All Generative Recommendation Framework for CPS Advertising

This article reviews recent advances in applying large language models to CPS advertising recommendation, outlines business requirements and core technical challenges, proposes an extensible multi‑task generative framework with explicit intent perception and multi‑objective optimization, and presents offline and online performance gains along with future research directions.

AI optimizationCPS AdvertisingLLM
0 likes · 13 min read
One4All Generative Recommendation Framework for CPS Advertising
JD Tech Talk
JD Tech Talk
Apr 27, 2025 · Artificial Intelligence

Breaking the Hourglass Phenomenon of Residual Quantization: Enhancing the Upper Bound of Generative Retrieval

This paper investigates the "sandglass" phenomenon in residual‑quantized semantic identifiers for generative search and recommendation, analyzes its causes of path sparsity and long‑tail token distribution, and proposes heuristic and adaptive token‑removal methods that substantially improve model performance in e‑commerce scenarios.

Recommendation systemsadaptive token removalgenerative retrieval
0 likes · 10 min read
Breaking the Hourglass Phenomenon of Residual Quantization: Enhancing the Upper Bound of Generative Retrieval
Alimama Tech
Alimama Tech
Apr 3, 2025 · Artificial Intelligence

UQABench: A Personalized QA Benchmark for Evaluating User Embeddings in LLM‑Driven Recommendation Systems

UQABench introduces the first benchmark for assessing high‑density user embeddings that serve as soft prompts in LLM‑driven recommendation, featuring a three‑stage pre‑train‑align‑evaluate pipeline, seven personalized QA tasks, and findings that transformer encoders, side‑information, simple linear adapters, and larger models markedly improve accuracy while cutting input tokens to about five percent.

AILLMRecommendation systems
0 likes · 12 min read
UQABench: A Personalized QA Benchmark for Evaluating User Embeddings in LLM‑Driven Recommendation Systems
58 Tech
58 Tech
Mar 11, 2025 · Artificial Intelligence

Applying Large Language Models to Real Estate Recommendation: Case Studies and Optimization Techniques

This article presents a comprehensive case study on how large language models are integrated into 58.com’s real‑estate recommendation platform, detailing challenges, data adaptation, prompt and parameter optimizations, embedding generation, conversational recommendation, and future directions for multimodal and generative recommendation systems.

AI optimizationReal EstateRecommendation systems
0 likes · 14 min read
Applying Large Language Models to Real Estate Recommendation: Case Studies and Optimization Techniques
Cognitive Technology Team
Cognitive Technology Team
Mar 6, 2025 · Artificial Intelligence

From Traditional Machine Learning to Deep Learning: A Comprehensive Guide to Algorithms, Feature Engineering, and Model Training

This article provides a step‑by‑step tutorial that walks readers through the fundamentals of traditional machine‑learning algorithms, feature‑engineering techniques, model training pipelines, evaluation metrics, and then advances to deep‑learning concepts such as MLPs, activation functions, transformers, and modern recommendation‑system models.

PythonRecommendation systemsTransformer
0 likes · 63 min read
From Traditional Machine Learning to Deep Learning: A Comprehensive Guide to Algorithms, Feature Engineering, and Model Training
DataFunSummit
DataFunSummit
Mar 5, 2025 · Artificial Intelligence

Evolution and Future Trends of Recommendation Systems: From Deep Learning to Large Language Models and AI Agents

This article reviews a decade of recommendation‑system research, outlines the shift from traditional listwise methods to deep‑learning models, discusses the impact of large language models and AI agents, and presents future directions such as multimodal interaction, responsible AI, cognitive modeling, and ecosystem integration.

AI agentsRecommendation systemsdeep learning
0 likes · 31 min read
Evolution and Future Trends of Recommendation Systems: From Deep Learning to Large Language Models and AI Agents
DataFunSummit
DataFunSummit
Feb 26, 2025 · Artificial Intelligence

Applying Multimodal Large Models to Music Recommendation at NetEase Cloud Music

This article details how NetEase Cloud Music leverages multimodal large language models to improve music recommendation across daily, personalized, and playlist scenarios by extracting rich audio, text, and visual features, addressing data skew, cold‑start challenges, and achieving measurable gains in user engagement and distribution efficiency.

Feature ExtractionNetEase Cloud MusicRecommendation systems
0 likes · 12 min read
Applying Multimodal Large Models to Music Recommendation at NetEase Cloud Music
Qunar Tech Salon
Qunar Tech Salon
Feb 17, 2025 · Artificial Intelligence

Evolution of Qunar Hotel Search Ranking: From LambdaMart to LambdaDNN and Multi‑Objective Optimization

The article details Qunar’s hotel search ranking system evolution, covering the shift from rule‑based sorting to LambdaMart, the adoption of LambdaDNN deep models, multi‑objective MMOE architectures, multi‑scenario integration, extensive feature engineering, and experimental results demonstrating significant offline and online performance gains.

Recommendation systemsdeep learningfeature selection
0 likes · 36 min read
Evolution of Qunar Hotel Search Ranking: From LambdaMart to LambdaDNN and Multi‑Objective Optimization
DataFunSummit
DataFunSummit
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 PlatformFeature StoreRAG
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
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
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
DataFunSummit
DataFunSummit
Feb 7, 2025 · Artificial Intelligence

Fusion Ranking and Multi-Objective Optimization in Recommendation Systems

This article introduces the role of ranking formulas in recommendation systems, compares sequence and value fusion methods, discusses multi‑objective trade‑offs, explains offline parameter search principles, and demonstrates the open‑source ParaDance framework for automated ranking formula optimization.

Recommendation systemsalgorithm engineeringmulti-objective
0 likes · 17 min read
Fusion Ranking and Multi-Objective Optimization in Recommendation Systems
DataFunTalk
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 IntelligenceGNNGraph Neural Networks
0 likes · 5 min read
Why Graph Neural Networks Are Suitable for Recommendation Systems
JD Retail Technology
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 predictionComputer Vision
0 likes · 20 min read
Tech Insight: Selected JD Retail Technology Papers in Artificial Intelligence (2024)
ZhongAn Tech Team
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
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.

LLMRecommendation systemscollaborative filtering
0 likes · 13 min read
Understanding Xiaohongshu’s Content Recommendation Mechanisms: NoteLLM and SSD
Kuaishou Tech
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
DataFunSummit
DataFunSummit
Jan 15, 2025 · Artificial Intelligence

Decentralized Distribution in Xiaohongshu: Strengthening Sideinfo, Multimodal Fusion, and Interest Exploration

This article details Xiaohongshu's technical approaches to solving decentralized content distribution by enhancing side‑information usage, integrating multimodal signals across the recommendation pipeline, applying graph‑based models, and implementing interest exploration and protection mechanisms, while also outlining future research directions.

Large ModelsRecommendation systemsdecentralized distribution
0 likes · 24 min read
Decentralized Distribution in Xiaohongshu: Strengthening Sideinfo, Multimodal Fusion, and Interest Exploration