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79 articles

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Machine Learning Algorithms & Natural Language Processing

Mar 26, 2026 · Artificial Intelligence

UniOD: A Single Model for Zero‑Training Cross‑Domain Anomaly Detection

UniOD introduces a universal outlier detection model that leverages historical labeled datasets to train one deep graph‑neural‑network‑based model, enabling plug‑and‑play anomaly detection on unseen domains without any retraining, and is backed by theoretical guarantees and extensive cross‑domain experiments.

Graph Neural NetworkUniODanomaly detection

0 likes · 10 min read

UniOD: A Single Model for Zero‑Training Cross‑Domain Anomaly Detection

HyperAI Super Neural

Feb 27, 2026 · Artificial Intelligence

SeaCast Delivers 15-Day Ocean Forecasts in 20 Seconds Using a Graph Neural Network

SeaCast, a graph‑neural‑network model developed by a European research team, generates 15‑day, 1/24° regional ocean forecasts in just 20 seconds on a single GPU, outperforming the traditional MedFS CPU‑based system in both speed and accuracy across multiple ocean variables.

GPU AccelerationGraph Neural NetworkSeaCast

0 likes · 15 min read

SeaCast Delivers 15-Day Ocean Forecasts in 20 Seconds Using a Graph Neural Network

Network Intelligence Research Center (NIRC)

Feb 3, 2026 · Artificial Intelligence

INCS: A DRL‑Based Intent‑Driven Network‑Wide Configuration Synthesis Framework

The article presents INCS, a novel framework that combines graph neural networks and deep reinforcement learning to achieve protocol‑agnostic, millisecond‑level, globally optimized network configuration synthesis, addressing scalability, protocol dependence, and lack of optimization in traditional SMT‑based methods, and demonstrates its superior performance on large‑scale topologies.

DDPGGraph Neural NetworkNetwork Synthesis

0 likes · 8 min read

INCS: A DRL‑Based Intent‑Driven Network‑Wide Configuration Synthesis Framework

Bighead's Algorithm Notes

Jan 10, 2026 · Artificial Intelligence

Key Quantitative AI Papers Jan 3‑9 2026: Portfolio Optimization, Equity Correlation Forecasting, and Index Tracking Review

This article summarizes three recent quantitative finance papers—introducing a decision‑oriented SPO paradigm for portfolio optimization, a hybrid transformer‑graph neural network for forecasting S&P 500 equity correlations, and a comprehensive review of modeling approaches for financial index tracking—highlighting their methods, datasets, and empirical findings.

AIGraph Neural NetworkQuantitative Finance

0 likes · 9 min read

Key Quantitative AI Papers Jan 3‑9 2026: Portfolio Optimization, Equity Correlation Forecasting, and Index Tracking Review

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%

Bighead's Algorithm Notes

Dec 2, 2025 · Artificial Intelligence

Dual-Relation Fusion Network (DRFN) for Accurate Stock Prediction

The paper introduces DRFN, a dual‑relation fusion network that jointly models static and dynamic stock relationships using multimodal BERT and GRU encodings, achieving significantly lower RMSE and MAE than baseline models on both Chinese and US market datasets.

BERTGRUGraph Neural Network

0 likes · 11 min read

Dual-Relation Fusion Network (DRFN) for Accurate Stock Prediction

Bighead's Algorithm Notes

Nov 16, 2025 · Artificial Intelligence

COGRASP: Multi‑Scale Stock Price Prediction Using Co‑Occurrence Graphs

This article reviews the COGRASP method, which builds dynamic co‑occurrence graphs from online sources, embeds them with graph neural networks, extracts short, medium, and long‑term patterns via attention‑based LSTMs, and aggregates these signals to achieve state‑of‑the‑art stock price prediction performance on real‑world CSI‑300 data.

ALSTMFinancial AIGraph Neural Network

0 likes · 14 min read

COGRASP: Multi‑Scale Stock Price Prediction Using Co‑Occurrence Graphs

Bighead's Algorithm Notes

Nov 9, 2025 · Artificial Intelligence

How Heuristic‑Guided Inverse Reinforcement Learning Boosts Portfolio Optimization

The article presents a heuristic‑guided inverse reinforcement learning framework that generates expert strategies respecting industry diversification and correlation constraints, employs a multi‑objective reward to balance return and risk, and uses a heterogeneous graph attention network to model stock relationships, achieving superior risk‑adjusted returns on CSI‑300, CSI‑500, NASDAQ‑100 and S&P‑500 benchmarks.

Financial AIGraph Neural Networkheuristic expert policy

0 likes · 13 min read

How Heuristic‑Guided Inverse Reinforcement Learning Boosts Portfolio Optimization

Bighead's Algorithm Notes

Nov 7, 2025 · Artificial Intelligence

Weekly AI Finance Paper Digest (Nov 1‑7 2025)

This digest summarizes three recent AI‑driven finance papers—DeltaLag’s dynamic lead‑lag detection, MS‑HGFN’s multi‑scale graph network for stock movement, and LiveTradeBench’s real‑time LLM trading benchmark—highlighting their methods, datasets, and performance gains.

Financial AIGraph Neural NetworkStock Prediction

0 likes · 8 min read

Weekly AI Finance Paper Digest (Nov 1‑7 2025)

HyperAI Super Neural

Nov 7, 2025 · Artificial Intelligence

How PLACER Tackles Atomic‑Level Modeling of Protein Conformational Heterogeneity

The PLACER graph‑neural‑network framework from David Baker’s lab generates atom‑accurate small‑molecule structures and protein‑ligand conformational ensembles, trained on large CSD and PDB datasets, achieving sub‑Å precision, outperforming traditional docking in many benchmarks and markedly improving enzyme‑design success rates.

Deep LearningGraph Neural NetworkPLACER

0 likes · 15 min read

How PLACER Tackles Atomic‑Level Modeling of Protein Conformational Heterogeneity

HyperAI Super Neural

Sep 15, 2025 · Artificial Intelligence

scSiameseClu Sets New SOTA on Unsupervised Single‑Cell Clustering Across 7 Datasets

The paper introduces scSiameseClu, a Siamese clustering framework that combines dual augmentation, siamese fusion, and optimal‑transport clustering to overcome representation collapse in scRNA‑seq data, and demonstrates state‑of‑the‑art performance on seven diverse single‑cell datasets and downstream annotation tasks.

Graph Neural NetworkSiamese Networkclustering

0 likes · 11 min read

scSiameseClu Sets New SOTA on Unsupervised Single‑Cell Clustering Across 7 Datasets

Architecture & Thinking

Sep 12, 2025 · Artificial Intelligence

How Knowledge Graphs Turn Large Language Models into Trustworthy Experts

Integrating structured knowledge graphs with generative AI provides traceable, explainable, and high‑precision reasoning across domains such as medicine, finance, and law, through techniques like Retrieval‑Augmented Generation, graph neural networks, and adaptive planning, dramatically reducing hallucinations and boosting expert‑level performance.

AI hallucinationGraph Neural NetworkKnowledge Graph

0 likes · 12 min read

How Knowledge Graphs Turn Large Language Models into Trustworthy Experts

Network Intelligence Research Center (NIRC)

Jun 29, 2025 · Artificial Intelligence

Multimodal AI Assistant Boosts Network Config: 96.6% Accuracy, 26× Labor Cut

The paper presents NLI2Conf, an intent‑driven network configuration model that fuses configuration files, topology and performance data via a multimodal interface, using large language and graph neural models to align natural‑language intents with forwarding and performance constraints, achieving 96.6% accuracy and a 26‑fold reduction in manual effort.

Graph Neural NetworkMultimodal AINLI2Conf

0 likes · 6 min read

Multimodal AI Assistant Boosts Network Config: 96.6% Accuracy, 26× Labor Cut

JD Cloud Developers

Sep 23, 2024 · Artificial Intelligence

How JD’s Advertising Lab Leverages Large‑Scale AI to Transform E‑Commerce Ads

JD's advertising research team combines deep learning, multimodal modeling, reinforcement‑learning auctions, and generative recommendation to boost ad relevance, improve long‑tail product exposure, and overcome large‑model inference challenges in a high‑traffic e‑commerce environment.

Graph Neural Networkadvertising AIe‑commerce

0 likes · 22 min read

How JD’s Advertising Lab Leverages Large‑Scale AI to Transform E‑Commerce Ads

Ximalaya Technology Team

Jul 12, 2024 · Artificial Intelligence

Multi-Path Recall and Ranking Techniques in Real-Time Bidding Advertising Systems

In real‑time bidding advertising, a multi‑path recall framework quickly filters billions of ads using parallel non‑personalized and personalized strategies—such as hot‑item rules, collaborative‑filtering, skip‑gram vectors, and GraphSAGE embeddings—while respecting targeting constraints, before a ranking stage optimizes eCPM, with effectiveness measured offline and online and future extensions planned with large language models.

AdvertisingGraph Neural Networkmachine learning

0 likes · 18 min read

Multi-Path Recall and Ranking Techniques in Real-Time Bidding Advertising Systems

DataFunTalk

Jun 24, 2024 · Artificial Intelligence

CausalMMM: Learning Causal Structure for Marketing Mix Modeling

The paper introduces CausalMMM, a variational inference framework that integrates Granger causality and graph neural networks to automatically discover heterogeneous causal structures in marketing mix modeling, enabling more accurate GMV prediction and actionable insights for diverse advertisers.

AdvertisingGMV predictionGraph Neural Network

0 likes · 15 min read

CausalMMM: Learning Causal Structure for Marketing Mix Modeling

AntTech

Jun 20, 2024 · Artificial Intelligence

Predicting Football Match Outcomes with Graph Neural Networks and Large Language Models: The “Smart Guess Football” Project

During the 2024 European Championship, TuGraph engineers built an interactive system called “Smart Guess Football” that combines graph computing, graph neural networks, transformers and large language models to model player relationships and predict match outcomes, achieving up to 71% accuracy on limited test matches.

AIGraph Neural NetworkSports Analytics

0 likes · 7 min read

Predicting Football Match Outcomes with Graph Neural Networks and Large Language Models: The “Smart Guess Football” Project

DataFunSummit

Jun 4, 2024 · Artificial Intelligence

Multimodal and Graph Neural Network Techniques for eBay Recommendation Systems

This article details eBay's practical experience integrating multimodal data and graph neural networks into its recommendation pipeline, covering pain‑point analysis, a twin‑tower multimodal embedding model with triplet loss and TransH, engineering design, experimental results, and key takeaways for future AI‑driven product development.

EmbeddingGNNGraph Neural Network

0 likes · 19 min read

Multimodal and Graph Neural Network Techniques for eBay Recommendation Systems

DataFunSummit

Apr 16, 2024 · Artificial Intelligence

Intelligent Risk Control: Definitions, Expert Systems, Algorithmic Systems, and Emerging AI Techniques

This article explains intelligent risk control as a synergy of expert experience and algorithmic decision‑making, outlines its definition, expert human systems, digital algorithmic systems, and explores advanced AI methods such as reinforcement learning, large language models with knowledge graphs, adversarial learning, graph neural networks, and a practical supply‑chain case study.

Graph Neural NetworkKnowledge Graphadversarial learning

0 likes · 11 min read

Intelligent Risk Control: Definitions, Expert Systems, Algorithmic Systems, and Emerging AI Techniques

DataFunSummit

Apr 6, 2024 · Information Security

Comprehensive Guide to Malicious Website Anti‑Fraud: Detection, Operation, and Modeling

This article provides a detailed overview of malicious website anti‑fraud, covering classification, development, operational tactics, revenue models, multi‑dimensional anomaly detection, and advanced counter‑measure models such as fingerprint, text, image, complex network, and multimodal approaches.

Graph Neural Networkanomaly detectionanti-fraud

0 likes · 16 min read

Comprehensive Guide to Malicious Website Anti‑Fraud: Detection, Operation, and Modeling

Network Intelligence Research Center (NIRC)

Nov 27, 2023 · Artificial Intelligence

How Deep Reinforcement Learning Shapes 15‑Minute City Community Planning

This article explains how a deep reinforcement learning model, built on a graph‑based representation of urban elements and trained with PPO, can automate land‑use and road planning to achieve Service, Ecology, and Traffic objectives for 15‑minute city neighborhoods.

15-minute cityGraph Neural NetworkPPO

0 likes · 9 min read

How Deep Reinforcement Learning Shapes 15‑Minute City Community Planning

DataFunSummit

Nov 24, 2023 · Artificial Intelligence

Cold-Start Content Recommendation Practices at Kuaishou

This article describes Kuaishou's approach to cold-start content recommendation, outlining the problems addressed, challenges in modeling sparse new videos, and solutions including graph neural networks, I2U retrieval, TDM hierarchical retrieval, bias correction, and future research directions.

Bias CorrectionGraph Neural NetworkKuaishou

0 likes · 19 min read

Cold-Start Content Recommendation Practices at Kuaishou

Alimama Tech

Nov 1, 2023 · Artificial Intelligence

BOMGraph: Boosting Multi-Scenario E-commerce Search with a Unified Graph Neural Network

BOMGraph introduces a unified heterogeneous graph neural network that jointly models text, image, and similar‑item search across multiple e‑commerce scenarios, using meta‑path‑guided attention, disentangled scenario‑specific and shared embeddings, and contrastive learning to alleviate sample sparsity, achieving consistent offline and online performance gains.

Graph Neural Networkcontrastive learninge‑commerce

0 likes · 13 min read

BOMGraph: Boosting Multi-Scenario E-commerce Search with a Unified Graph Neural Network

DataFunTalk

Oct 11, 2023 · Artificial Intelligence

Kuaishou Content Cold-Start Recommendation: Challenges, Modeling Solutions, and Future Directions

This article presents Kuaishou's approach to solving the content cold-start problem by analyzing its impact on video growth, detailing the challenges of sparse and biased training data, and describing a suite of graph‑neural‑network, I2U/U2I, TDM, and debiasing techniques that improve early video exposure and long‑term ecosystem health.

Graph Neural NetworkI2UKuaishou

0 likes · 18 min read

Kuaishou Content Cold-Start Recommendation: Challenges, Modeling Solutions, and Future Directions

AntTech

Aug 25, 2023 · Artificial Intelligence

LayoutGCN: A Lightweight Graph Convolutional Network for Visually Rich Document Understanding

LayoutGCN is a lightweight, graph‑based framework that jointly encodes text, layout, and image features of visually rich documents, achieving competitive performance on multiple downstream tasks while drastically reducing model size and computational cost, making it suitable for edge deployment.

Graph Neural NetworkLayoutGCNdocument understanding

0 likes · 24 min read

LayoutGCN: A Lightweight Graph Convolutional Network for Visually Rich Document Understanding

DataFunSummit

Aug 22, 2023 · Artificial Intelligence

Applying Artificial Intelligence to Cross‑Border Risk Control: Practices and Insights

This article presents how artificial intelligence is applied to cross‑border risk control, covering the company background, intelligent risk‑prevention architecture, transaction and marketing fraud scenarios, model design, data challenges, and practical Q&A insights for overseas fraud mitigation.

AIGraph Neural Networkcross-border

0 likes · 18 min read

Applying Artificial Intelligence to Cross‑Border Risk Control: Practices and Insights

Network Intelligence Research Center (NIRC)

Aug 15, 2023 · Artificial Intelligence

Neural Networks for Rapid Network Configuration: A Concise Overview

The article presents a neural‑algorithmic reasoning approach that replaces slow SMT‑based network configuration tools with a graph‑neural‑network model, describing dataset creation, model architecture, and experiments that show 20‑to‑490× speedups while maintaining over 92% configuration consistency on large topologies.

Graph Neural NetworkNetwork ConfigurationNetwork Synthesis

0 likes · 5 min read

Neural Networks for Rapid Network Configuration: A Concise Overview

Alimama Tech

Aug 9, 2023 · Artificial Intelligence

Highlights of Alibaba Mama Team Papers Accepted at CIKM 2023

Eight Alibaba Mama team papers accepted at CIKM 2023 present advances such as task‑specific bottom‑representation networks for recommendation, a unified GNN for multi‑scenario e‑commerce search, multi‑slot bid shading, consistency‑oriented pre‑ranking, bias‑mitigating CTR prediction, efficient progressive‑sampling self‑attention, delayed‑feedback conversion modeling, and hybrid contrastive multi‑scenario ad ranking.

AICTR predictionGraph Neural Network

0 likes · 13 min read

Highlights of Alibaba Mama Team Papers Accepted at CIKM 2023

Alimama Tech

Aug 9, 2023 · Artificial Intelligence

End-to-End Inventory Prediction and Contract Allocation for Guaranteed Delivery Advertising

The paper introduces Neural Lagrangian Selling, an end‑to‑end framework that jointly learns traffic forecasting and contract inventory allocation by embedding a differentiable Lagrangian solver and a graph convolutional network into a neural model, achieving higher prediction accuracy, fulfillment rates, utilization, and revenue than two‑stage and other methods.

Graph Neural NetworkNeural Networksend-to-end learning

0 likes · 16 min read

End-to-End Inventory Prediction and Contract Allocation for Guaranteed Delivery Advertising

DataFunSummit

Jul 31, 2023 · Artificial Intelligence

Knowledge Graph based Graph Neural Network Reasoning: From KG Background to GNN for KG and KG for GNN

This article introduces the fundamentals of knowledge graphs, explains how graph neural networks can be adapted for knowledge graph reasoning, presents specialized GNN designs such as CompGCN and RED‑GNN, and discusses experimental results, interpretability, efficiency improvements, and future research directions.

Graph Neural NetworkKG reasoningKnowledge Graph

0 likes · 11 min read

Knowledge Graph based Graph Neural Network Reasoning: From KG Background to GNN for KG and KG for GNN

DataFunSummit

Jun 21, 2023 · Artificial Intelligence

Graph‑Enhanced Node Representation for Cold‑Start Recommendation: Neighbour‑Enhanced YouTubeDNN

This article proposes a graph‑based node representation method that combines static attribute graphs and dynamic interaction graphs with multi‑level attention to alleviate user and item cold‑start problems in recommendation systems, achieving notable AUC improvements on sparsified MovieLens datasets.

EmbeddingGraph Neural NetworkMovieLens

0 likes · 9 min read

Graph‑Enhanced Node Representation for Cold‑Start Recommendation: Neighbour‑Enhanced YouTubeDNN

Architect

May 31, 2023 · Artificial Intelligence

Applying Graph Neural Networks for Anti‑Cheat in Activity Scenarios

This article presents how graph neural network models such as GCN and SCGCN are employed to detect and recall cheating groups in user‑invitation (master‑apprentice) activity scenarios, addressing the lack of relational features and low sample purity, and demonstrates significant recall improvements through multi‑graph fusion techniques.

GCNGraph Neural NetworkSCGCN

0 likes · 12 min read

Applying Graph Neural Networks for Anti‑Cheat in Activity Scenarios

Ctrip Technology

May 25, 2023 · Artificial Intelligence

Graph-Based Unsupervised Model for Detecting Malicious Account Clusters in Registration Risk Control

This article presents a graph‑neural‑network driven, unsupervised approach that builds heterogeneous user‑feature graphs, learns node weights, constructs user‑user similarity graphs, and applies threshold‑based clustering to identify abnormal registration clusters for fraud detection in Ctrip's business travel platform.

Graph Neural NetworkUnsupervised Learninganomaly detection

0 likes · 12 min read

Graph-Based Unsupervised Model for Detecting Malicious Account Clusters in Registration Risk Control

DataFunSummit

Mar 4, 2023 · Artificial Intelligence

Graph Neural Network-Based Knowledge Graph Reasoning – Talk by Zhang Yongqi at DataFun Summit 2023

On March 18, 2023, Dr. Zhang Yongqi will deliver an online talk at DataFun Summit 2023 about knowledge‑graph reasoning using graph neural networks, covering recent advances, applications, challenges, and offering free registration for participants interested in AI and knowledge‑graph technologies.

AIDataFunGraph Neural Network

0 likes · 4 min read

Graph Neural Network-Based Knowledge Graph Reasoning – Talk by Zhang Yongqi at DataFun Summit 2023

DataFunTalk

Feb 28, 2023 · Artificial Intelligence

Event‑Aware Graph Extraction and Adaptive Clustering‑Gain Network for Insurance Creative Recommendation

This article presents a comprehensive study on insurance creative recommendation, introducing an event‑aware graph extractor, a heterogeneous graph construction, and an adaptive clustering‑gain network that together address data sparsity, counterfactual samples, and cross‑industry cold‑start challenges, achieving significant AUC improvements in experiments.

AIAdvertisingGraph Neural Network

0 likes · 15 min read

Event‑Aware Graph Extraction and Adaptive Clustering‑Gain Network for Insurance Creative Recommendation

DataFunSummit

Jul 29, 2022 · Artificial Intelligence

Integrating Knowledge Graphs with Neural Networks: Generative Pre‑Training, Differentiable Reasoning, and Fuzzy Logic Query Embedding

This article reviews recent work on combining knowledge graphs with neural networks, covering generative self‑supervised graph neural network pre‑training, differentiable logical reasoning over graphs, and a fuzzy‑logic based query‑embedding model that improves open‑domain question answering, especially for rare relations.

Graph Neural NetworkKnowledge GraphOpen Domain QA

0 likes · 22 min read

Integrating Knowledge Graphs with Neural Networks: Generative Pre‑Training, Differentiable Reasoning, and Fuzzy Logic Query Embedding

DataFunSummit

Jul 13, 2022 · Artificial Intelligence

Construction and Application of Meituan's Life Service Knowledge Graph

This article details Meituan's "Meituan Brain" initiative, describing the roadmap and techniques for building large-scale life‑service knowledge graphs—including tag and dish graphs—through data mining, semantic extraction, synonym discovery, graph neural networks, and their integration into search, recommendation, and question‑answering systems.

AIGraph Neural NetworkKnowledge Graph

0 likes · 14 min read

Construction and Application of Meituan's Life Service Knowledge Graph

DataFunSummit

Jul 9, 2022 · Artificial Intelligence

Knowledge Graph Application Cases: Meituan Brain, Sage Knowledge Base, and Other Industry Examples

This article presents several mature knowledge‑graph application cases, including Meituan’s large‑scale “Meituan Brain” for lifestyle services, the Sage Knowledge Base platform by Fourth Paradigm, and additional examples in recommendation, medical, QA, and power‑industry domains, highlighting methods, challenges, and model designs.

AIGraph Neural NetworkKnowledge Graph

0 likes · 11 min read

Knowledge Graph Application Cases: Meituan Brain, Sage Knowledge Base, and Other Industry Examples

DataFunSummit

Jul 6, 2022 · Artificial Intelligence

Knowledge Graph Application Cases: Meituan Brain, Sage Knowledge Base, and Other Industry Scenarios

This article reviews several mature knowledge‑graph applications, describing Meituan’s large‑scale “Meituan Brain” for lifestyle services, the Fourth Paradigm’s Sage Knowledge Base platform with various representation‑learning models, and additional use cases in recommendation, QA, drug discovery, and power‑grid domains.

AI applicationsGraph Neural NetworkKnowledge Graph

0 likes · 11 min read

Knowledge Graph Application Cases: Meituan Brain, Sage Knowledge Base, and Other Industry Scenarios

NetEase Cloud Music Tech Team

Jul 6, 2022 · Industry Insights

Inside NetEase Cloud Music’s MLOps: Scaling AI with VK, ECI, and Ceph

This article details NetEase Cloud Music’s four‑layer machine‑learning platform architecture, covering resource provisioning with Visual Kubelet and Alibaba Cloud ECI, Ceph storage optimizations, TensorFlow migration, large‑scale graph neural network support, and end‑to‑end workflow tooling that together enable efficient, cost‑effective AI development and deployment.

CephGPUGraph Neural Network

0 likes · 24 min read

Inside NetEase Cloud Music’s MLOps: Scaling AI with VK, ECI, and Ceph

DataFunSummit

Jun 22, 2022 · Artificial Intelligence

Generating and Applying Social Relationship Graphs for Video Understanding

This talk presents recent research on integrating dynamic analysis and graph machine learning to generate social relationship graphs from video, detailing hierarchical graph convolution networks, multimodal feature fusion, weakly supervised training, experimental results, and applications such as enhanced video retrieval and storyline understanding.

Graph Neural NetworkWeak Supervisionsocial relationship graph

0 likes · 11 min read

Generating and Applying Social Relationship Graphs for Video Understanding

Alimama Tech

Jun 15, 2022 · Artificial Intelligence

Multi-modal Multi-query Search Session Modeling with Heterogeneous Graph Neural Networks

The paper introduces MUVCOG, a heterogeneous graph neural network that models multi‑modal, multi‑query search sessions on Mobile Taobao by jointly learning attention‑based global and hierarchical local views through contrastive pre‑training, yielding universal session embeddings that markedly improve CTR prediction, query recommendation, and intent classification.

Graph Neural Networkcontrastive learningmulti-modal

0 likes · 15 min read

Multi-modal Multi-query Search Session Modeling with Heterogeneous Graph Neural Networks

DataFunTalk

Jun 3, 2022 · Artificial Intelligence

Construction and Application of Meituan's Life‑Service Knowledge Graph

This article explains Meituan's 'Meituan Brain' initiative, detailing the construction of life‑service knowledge graphs—including tag and dish graphs—through data mining, semantic extraction, synonym discovery, graph labeling, and applications such as open QA, search ranking, and recommendation using AI and GNN techniques.

AIGraph Neural NetworkKnowledge Graph

0 likes · 13 min read

DataFunTalk

May 20, 2022 · Artificial Intelligence

Hierarchical Graph Convolutional Networks for Video Social Relationship Modeling

This article presents a multimodal approach that combines dynamic analysis and graph machine learning to generate and apply social relationship graphs in videos, detailing problem background, graph generation modules, applications such as video retrieval, experimental results, and future research directions.

AIGraph Neural NetworkWeak Supervision

0 likes · 11 min read

Hierarchical Graph Convolutional Networks for Video Social Relationship Modeling

DataFunTalk

Apr 10, 2022 · Big Data

Angel Graph: A Large-Scale Graph Computing Platform by Tencent

This article introduces Tencent's Angel Graph platform, detailing its evolution from early versions to a mature large‑scale graph computing system, its architecture combining Angel PS with Spark and PyTorch, data and model partitioning strategies, communication and computation optimizations, stability features, usability, and real‑world applications.

Angel GraphGraph Neural NetworkSpark

0 likes · 15 min read

Angel Graph: A Large-Scale Graph Computing Platform by Tencent

DataFunSummit

Apr 2, 2022 · Artificial Intelligence

Graph-Based I2I Recall for Short Video Recommendation at Kuaishou

This article presents Kuaishou's graph‑based item‑to‑item (I2I) recall pipeline for short‑video recommendation, detailing the business challenges, pipeline architecture, optimization techniques such as similarity‑measure tricks, graph structure learning, edge‑weight learning, and future research directions.

AIEmbeddingGraph Neural Network

0 likes · 16 min read

Graph-Based I2I Recall for Short Video Recommendation at Kuaishou

DataFunSummit

Mar 16, 2022 · Artificial Intelligence

Semantic Search Recall Techniques at JD: Dual‑Tower Model, Graph Model, Synonym Recall, and Joint Index Training

This article presents JD's end‑to‑end semantic search recall pipeline, covering multi‑stage recall, a dual‑tower embedding model with multi‑head attention, a heterogeneous graph neural network (SearchGCN), a transformer‑based synonym generation system, and a joint index‑training approach that integrates product quantization to improve recall accuracy and efficiency.

Deep LearningGraph Neural Networkdual-tower model

0 likes · 17 min read

Semantic Search Recall Techniques at JD: Dual‑Tower Model, Graph Model, Synonym Recall, and Joint Index Training

DataFunTalk

Mar 9, 2022 · Artificial Intelligence

Semantic Search Recall Techniques at JD: Dual‑Tower Model, Graph Model, Synonym Recall, and Index Joint Training

The talk presents JD's end‑to‑end semantic search recall pipeline, covering multi‑stage retrieval, a dual‑tower embedding model with multi‑head attention, a heterogeneous graph neural network for low‑frequency items, automatic synonym generation via transformer models, and a joint training approach that integrates product quantization directly into the model to improve accuracy and efficiency.

Deep LearningGraph Neural Networkdual-tower model

0 likes · 16 min read

Semantic Search Recall Techniques at JD: Dual‑Tower Model, Graph Model, Synonym Recall, and Index Joint Training

DataFunSummit

Feb 21, 2022 · Artificial Intelligence

Advances in E‑commerce Search: Embedding, Knowledge Graphs, and Retrieval Models

This article reviews recent research on e‑commerce search, covering transformer‑based complementary rankings, Alibaba's cognitive concept net and its extension, joint deep retrieval with product quantization, personalized semantic retrieval, multi‑granularity deep semantic retrieval, and graph‑attention networks for long‑tail shop search.

AIEmbeddingGraph Neural Network

0 likes · 12 min read

Advances in E‑commerce Search: Embedding, Knowledge Graphs, and Retrieval Models

DataFunTalk

Dec 29, 2021 · Artificial Intelligence

Entity Alignment in Product Knowledge Graphs: Techniques and Applications

This article presents a comprehensive overview of building and applying product knowledge graphs for e‑commerce, covering background, recent advances in graph neural network‑based entity alignment, online prediction pipelines, data construction, evaluation metrics, attribute extraction, and future research directions.

Graph Neural NetworkKnowledge Graphattribute extraction

0 likes · 23 min read

Entity Alignment in Product Knowledge Graphs: Techniques and Applications

DataFunTalk

Dec 18, 2021 · Artificial Intelligence

Adaptive Mutual Supervision Multi‑Task Graph Neural Network for Fine‑Grained Urban Traffic Demand Prediction

This work proposes an adaptive mutual‑supervision multi‑task graph neural network that captures spatio‑temporal dynamics and heterogeneous group behaviors to predict fine‑grained urban travel demand, demonstrating over 10% performance gains on real‑world Beijing and Shanghai datasets compared with classic baselines.

Deep LearningGraph Neural NetworkTraffic Prediction

0 likes · 24 min read

Adaptive Mutual Supervision Multi‑Task Graph Neural Network for Fine‑Grained Urban Traffic Demand Prediction

DataFunSummit

Dec 18, 2021 · Artificial Intelligence

Adaptive Mutual Supervision Multi‑Task Graph Neural Network for Fine‑Grained Urban Traffic Demand Prediction

This work introduces a novel adaptive mutual‑supervision multi‑task graph neural network that captures spatio‑temporal dynamics and group‑specific travel patterns, achieving over 10% improvement in short‑term traffic demand forecasts across heterogeneous urban populations.

Graph Neural Networkadaptive supervisionmulti-task learning

0 likes · 22 min read

DataFunSummit

Nov 23, 2021 · Artificial Intelligence

Travel Intention‑Aware Out‑of‑Town POI Recommendation (TRAINOR) Framework

This article presents TRAINOR, a travel‑intention‑aware out‑of‑town POI recommendation framework that tackles cold‑start, interest‑drift, and geographical gaps by jointly modeling hometown preferences with graph neural networks, neural topic models for travel intention, and matrix‑factorization‑based out‑of‑town preference transfer, and validates its superiority through extensive cross‑city experiments.

Graph Neural NetworkPOI recommendationcold start

0 likes · 16 min read

Travel Intention‑Aware Out‑of‑Town POI Recommendation (TRAINOR) Framework

DataFunTalk

Oct 29, 2021 · Artificial Intelligence

Travel Intention‑Aware Out‑of‑Town POI Recommendation (TRAINOR) Framework

This article proposes TRAINOR, a travel‑intention‑aware out‑of‑town POI recommendation framework that tackles cold‑start and interest‑drift challenges by integrating graph neural networks for hometown preference, neural topic models for generic travel intentions, personalized intention inference, geographic modeling, and a preference‑transfer MLP, validated on real cross‑city check‑in data with superior recall performance.

Graph Neural NetworkPOI recommendationcold start

0 likes · 15 min read

Kuaishou Tech

Oct 12, 2021 · Artificial Intelligence

Concept‑aware Denoising Graph Neural Network (CONDE) for Short Video Recommendation

CONDE, a concept‑aware denoising graph neural network proposed by Wuhan University and Kuaishou, leverages heterogeneous three‑part graphs, attention‑based graph convolutions, and Gumbel‑Softmax‑driven edge sampling to filter noisy user‑video interactions, achieving up to 6 % AUC improvement on short‑video and e‑commerce recommendation tasks.

AIDenoisingGraph Neural Network

0 likes · 10 min read

Concept‑aware Denoising Graph Neural Network (CONDE) for Short Video Recommendation

DataFunSummit

Sep 19, 2021 · Artificial Intelligence

Graph Computing for Risk Control in WeChat Pay: From Feature Engineering to Network Analysis

This talk explains how WeChat Pay leverages graph algorithms, graph databases, and graph neural networks to combat fraud at massive scale, covering new risk‑control perspectives, the three‑pillar graph computing platform, practical applications, and the team’s innovations in algorithm design and deployment.

Graph Neural NetworkWeChat Paygraph computing

0 likes · 18 min read

Graph Computing for Risk Control in WeChat Pay: From Feature Engineering to Network Analysis

DataFunSummit

Sep 3, 2021 · Artificial Intelligence

Personalized Marketing Platform for Travel: Architecture, Algorithms, and Cold‑Start Solutions at Alibaba Fliggy

This article presents Alibaba Fliggy's personalized marketing platform for travel, detailing its multi‑scene architecture, user‑session modeling, graph‑based recommendation algorithms, cold‑start strategies, cross‑domain user mapping, and a hierarchical travel‑play tag system that together enable large‑scale, real‑time, thousand‑person‑one‑face marketing.

Graph Neural NetworkTravelcold start

0 likes · 20 min read

Personalized Marketing Platform for Travel: Architecture, Algorithms, and Cold‑Start Solutions at Alibaba Fliggy

TiPaiPai Technical Team

Jul 2, 2021 · Artificial Intelligence

How Graph Neural Networks Revolutionize Arbitrary‑Shaped Text Detection

This article reviews two recent computer‑vision approaches—DRRG and STKM—that combine CNN backbones with graph‑based relational reasoning and self‑attention to achieve state‑of‑the‑art detection of arbitrarily shaped text in images.

CNNComputer VisionDeep Learning

0 likes · 11 min read

How Graph Neural Networks Revolutionize Arbitrary‑Shaped Text Detection

Didi Tech

Jun 4, 2021 · Artificial Intelligence

Graph Convolutional Network for Shared Bike Demand Forecasting: Time Series Modeling and Multi‑Task Learning

The paper presents a graph convolutional network approach that leverages multi‑task learning and spectral graph convolutions to forecast shared‑bike inflow, outflow, and demand gaps across a city’s non‑Euclidean parking network, demonstrating improved accuracy over traditional time‑series baselines while noting scalability and directional graph limitations.

Demand ForecastingGCNGraph Neural Network

0 likes · 13 min read

Graph Convolutional Network for Shared Bike Demand Forecasting: Time Series Modeling and Multi‑Task Learning

Alimama Tech

May 27, 2021 · Artificial Intelligence

Explicit Semantic Cross Feature Learning via Pre-trained Graph Neural Networks for CTR Prediction (PCF‑GNN)

PCF‑GNN builds a heterogeneous graph of feature nodes and learns edge statistics via pre‑training, enabling it to infer unseen cross‑features, reduce storage by over 50%, and consistently improve CTR prediction AUC compared to implicit and explicit baselines, with proven online gains.

Graph Neural NetworkRecommendation Systemscross feature

0 likes · 12 min read

Explicit Semantic Cross Feature Learning via Pre-trained Graph Neural Networks for CTR Prediction (PCF‑GNN)

58 Tech

May 10, 2021 · Information Security

Marketing Anti‑Fraud Algorithm Framework and Practice at 58.com

This article details the design, implementation, and evaluation of a multi‑layer anti‑fraud system for 58.com’s marketing activities, covering data and feature engineering, unsupervised and supervised models, graph‑based community detection, and semi‑supervised graph neural networks, with empirical results demonstrating their effectiveness.

Graph Neural NetworkMarketingUnsupervised Learning

0 likes · 18 min read

Marketing Anti‑Fraud Algorithm Framework and Practice at 58.com

AntTech

Mar 21, 2021 · Artificial Intelligence

Hubble Intelligent Audience Platform: Three‑Generation Algorithm Evolution for Mobile Marketing

The article describes the Hubble Intelligent Audience Platform’s three‑generation algorithmic evolution—starting from a DSSM‑based model, moving to an asynchronous GNN plus lightweight learning architecture, and finally integrating incremental learning with meta‑weighting—to improve audience expansion for mobile marketing campaigns.

AIGraph Neural NetworkMobile Marketing

0 likes · 14 min read

Hubble Intelligent Audience Platform: Three‑Generation Algorithm Evolution for Mobile Marketing

DataFunTalk

Mar 20, 2021 · Artificial Intelligence

Model‑Based Recall in Momo's Social Recommendation: Technical Exploration and Practical Applications

This article presents a comprehensive technical overview of Momo's model‑based recall system for social recommendation, detailing the underlying user‑scenario behavior models, social graph embeddings, multimodal content semantics, and deployment results that improve matching relevance and user interaction rates.

EmbeddingGraph Neural NetworkMomo

0 likes · 19 min read

Model‑Based Recall in Momo's Social Recommendation: Technical Exploration and Practical Applications

AntTech

Mar 19, 2021 · Artificial Intelligence

Network Effects in Marketing: Graph Neural Network–Based Relationship Prediction and Clustered A/B Testing

This article presents a graph‑neural‑network approach to predict user influence, cluster users with distributed Louvain methods, and conduct network‑aware A/B experiments that accurately evaluate large‑scale marketing campaigns despite strong network effects.

A/B testingBig DataGraph Neural Network

0 likes · 9 min read

Network Effects in Marketing: Graph Neural Network–Based Relationship Prediction and Clustered A/B Testing

DataFunTalk

Feb 9, 2021 · Artificial Intelligence

Multimodal AI Research: Video-Aware Dialog, Dual-Channel Reasoning, and Multimodal Machine Translation

This article surveys recent multimodal AI research, covering video scene‑aware dialog with a GPT‑2 based unified pre‑training framework, dual‑channel multi‑hop reasoning for visual dialog, capsule‑network‑enhanced multimodal machine translation, and graph‑neural‑network‑driven multimodal translation, highlighting experimental results and future directions.

Graph Neural NetworkMultimodal AIMultimodal Learning

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Multimodal AI Research: Video-Aware Dialog, Dual-Channel Reasoning, and Multimodal Machine Translation

DataFunTalk

Feb 4, 2021 · Artificial Intelligence

Cross‑Session Aware Temporal Convolutional Network (CA‑TCN) for Session‑Based Recommendation

The article introduces the CA‑TCN model, which combines cross‑session item graphs, a temporal convolutional network, and a session‑context graph to capture both item‑level and session‑level cross‑session influences, achieving state‑of‑the‑art performance on benchmark session‑based recommendation datasets.

Deep LearningGraph Neural NetworkTemporal Convolutional Network

0 likes · 17 min read

Cross‑Session Aware Temporal Convolutional Network (CA‑TCN) for Session‑Based Recommendation

Meituan Technology Team

Jan 28, 2021 · Artificial Intelligence

Trajectory Prediction Algorithm for Autonomous Vehicles: Winning Solutions in NeurIPS 2020 INTERPRET Challenge

Meituan’s unmanned delivery team secured first place in the Generalizability track and second in the Regular track of the NeurIPS 2020 INTERPRET trajectory‑prediction challenge by employing a mixed‑attention graph‑transformer with dual‑channel GRU and adaptive map processing, achieving ADEs of 0.5339 m and 0.1912 m respectively.

Graph Neural NetworkNeurIPSautonomous vehicles

0 likes · 15 min read

Trajectory Prediction Algorithm for Autonomous Vehicles: Winning Solutions in NeurIPS 2020 INTERPRET Challenge

JD Tech Talk

Jan 28, 2021 · Artificial Intelligence

Spatial‑Temporal Graph Diffusion Network for City Traffic Flow Forecasting

This article introduces a hierarchical graph neural network model that jointly captures multi‑scale temporal patterns and global spatial context for urban traffic flow prediction, demonstrates its superiority over existing methods on multiple public datasets, and validates each component through extensive ablation studies.

Deep LearningGraph Neural Networkattention

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Spatial‑Temporal Graph Diffusion Network for City Traffic Flow Forecasting

Meituan Technology Team

Dec 10, 2020 · Artificial Intelligence

Cross‑Session Aware Temporal Convolutional Network (CA‑TCN) for Session‑Based Recommendation

The Cross‑Session Aware Temporal Convolutional Network (CA‑TCN) combines a cross‑session item graph, a dilated temporal convolutional network, and a session‑context graph to capture both global cross‑session signals and positional order, achieving state‑of‑the‑art recommendation performance on benchmarks and slated for deployment in Meituan’s e‑commerce platforms.

Deep LearningGraph Neural NetworkTemporal Convolutional Network

0 likes · 17 min read

iQIYI Technical Product Team

Oct 30, 2020 · Artificial Intelligence

Heterogeneous Mini-Graph Neural Network for Fraud Invitation Detection

HMGNN introduces hyper-nodes to connect many small heterogeneous mini-graphs, uses attention-weighted heterogeneous convolution and residual feature transmission, achieving superior fraud invitation detection on iQIYI and Cora datasets compared to traditional GNNs and other models.

AIGraph Neural NetworkHypergraph

0 likes · 10 min read

Heterogeneous Mini-Graph Neural Network for Fraud Invitation Detection

DataFunTalk

Oct 28, 2020 · Artificial Intelligence

All-Rounder Recall Representation Algorithm Practice

This article presents a comprehensive overview of NetEase Yanxuan’s recall representation algorithms, detailing problem definition, model value, iterative implementations—including session-based embedding, GCN, GraphSAGE, LightGCN, and multi-interest models—along with engineering solutions, performance comparisons, and real-world deployment outcomes in search and recommendation systems.

EmbeddingGraph Neural NetworkSearch

0 likes · 16 min read

All-Rounder Recall Representation Algorithm Practice

JD Tech Talk

Aug 21, 2020 · Artificial Intelligence

JD Digits' Intelligent Anti‑Fraud Platform: AI‑Driven Real‑Time Fraud Detection and Knowledge‑Graph Solutions

JD Digits' intelligent anti‑fraud platform leverages machine learning, big‑data processing, graph neural networks and small‑sample knowledge‑graph algorithms to provide millisecond‑level, real‑time protection across 600+ scenarios, while also offering AI‑powered solutions to banks and publishing research at top conferences.

AIGraph Neural NetworkKnowledge Graph

0 likes · 6 min read

JD Digits' Intelligent Anti‑Fraud Platform: AI‑Driven Real‑Time Fraud Detection and Knowledge‑Graph Solutions

ITPUB

Aug 7, 2020 · Artificial Intelligence

How BC‑GNN Improves Temporal Action Proposals with Boundary‑Content Graph Modeling

The paper introduces Boundary Content Graph Neural Network (BC‑GNN), a graph‑based approach that jointly models boundary and content predictions to generate more accurate temporal action proposals and reliable confidence scores, achieving state‑of‑the‑art results on ActivityNet‑1.3 and THUMOS‑14.

BC-GNNECCV2020Graph Neural Network

0 likes · 12 min read

How BC‑GNN Improves Temporal Action Proposals with Boundary‑Content Graph Modeling

DataFunTalk

Aug 3, 2020 · Artificial Intelligence

Personalized Marketing Platform for Travel Scenarios: Architecture, Algorithms, and Cold‑Start Solutions at Alibaba Fliggy

This article presents Alibaba Fliggy's personalized marketing platform for travel, detailing its architecture, scenario and functional abstractions, user‑modeling pipelines, full‑stack traffic control, cold‑start techniques, cross‑domain mapping, heterogeneous graph modeling, and a hierarchical travel‑play tag system to achieve thousand‑person‑one‑face recommendation across daily and promotional scenes.

Graph Neural NetworkTravelcold start

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Personalized Marketing Platform for Travel Scenarios: Architecture, Algorithms, and Cold‑Start Solutions at Alibaba Fliggy

DataFunTalk

Jul 17, 2020 · Artificial Intelligence

WeChat "Look" Content Recall Architecture and Deep Learning Techniques

This article details the technical architecture behind WeChat's "Look" content recall, covering content sourcing, profiling, multimodal tagging, knowledge‑graph representations, propensity and target detection, multi‑stage recall pipelines, and a range of deep learning models including sequence, translation, BERT, dual‑tower, hybrid, and graph neural network approaches.

Deep LearningGraph Neural NetworkWeChat AI

0 likes · 32 min read

WeChat "Look" Content Recall Architecture and Deep Learning Techniques

DataFunTalk

Apr 12, 2020 · Artificial Intelligence

Wang Zhe’s Machine Learning Notes – Answers to Frequently Asked Questions on Recommendation Systems

In this article, Wang Zhe addresses fifteen common questions about recommendation systems, covering topics such as building cross‑domain knowledge, the role of deep reinforcement learning, handling sparse or low‑sample data, offline‑online evaluation, knowledge graphs, graph neural networks, model interpretability, large‑scale ID embedding, and career advice for engineers.

Deep LearningGraph Neural NetworkKnowledge Graph

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Wang Zhe’s Machine Learning Notes – Answers to Frequently Asked Questions on Recommendation Systems

Alibaba Cloud Developer

Mar 24, 2020 · Artificial Intelligence

How Knowledge Graphs and GNNs Boost HS Code Classification Accuracy

This article explores how integrating unstructured business data into structured knowledge graphs and applying graph neural networks can overcome deep‑learning bottlenecks in NLP, dramatically improving HS‑code product classification accuracy from around 60% to over 75% through richer reasoning and multimodal knowledge.

AIGNNGraph Neural Network

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How Knowledge Graphs and GNNs Boost HS Code Classification Accuracy

JD Tech Talk

Mar 16, 2020 · Artificial Intelligence

JD Digits' Self‑Developed Intelligent Anti‑Fraud Platform and AI‑Powered Account Security Guarantee

JD Digits explains how its AI‑driven anti‑fraud platform, featuring automatic adversarial machine learning and graph neural networks, underpins a new one‑million‑yuan account security guarantee that proactively protects users from invisible financial fraud while improving the overall user experience.

AIGraph Neural Networkaccount security

0 likes · 10 min read

JD Digits' Self‑Developed Intelligent Anti‑Fraud Platform and AI‑Powered Account Security Guarantee

Hulu Beijing

Jan 3, 2020 · Artificial Intelligence

How Dynamically Pruned Message Passing Networks Revolutionize Large‑Scale Knowledge Graph Reasoning

The Hulu AI team’s ICLR‑2020 paper introduces a consciousness‑prior‑driven graph neural network that dynamically prunes message‑passing subgraphs, achieving state‑of‑the‑art results on large‑scale knowledge‑graph completion tasks while improving interpretability and computational efficiency.

AI reasoningGraph Neural NetworkKnowledge Graph

0 likes · 7 min read

How Dynamically Pruned Message Passing Networks Revolutionize Large‑Scale Knowledge Graph Reasoning