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Ximalaya Technology Team
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.

Rankingadvertisinggraph neural network
0 likes · 18 min read
Multi-Path Recall and Ranking Techniques in Real-Time Bidding Advertising Systems
DataFunTalk
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.

GMV predictionadvertisingcausal inference
0 likes · 15 min read
CausalMMM: Learning Causal Structure for Marketing Mix Modeling
AntTech
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.

AISports Analyticsfootball prediction
0 likes · 7 min read
Predicting Football Match Outcomes with Graph Neural Networks and Large Language Models: The “Smart Guess Football” Project
DataFunSummit
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.

EmbeddingGNNMachine Learning
0 likes · 19 min read
Multimodal and Graph Neural Network Techniques for eBay Recommendation Systems
DataFunSummit
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.

adversarial learningartificial intelligencegraph neural network
0 likes · 11 min read
Intelligent Risk Control: Definitions, Expert Systems, Algorithmic Systems, and Emerging AI Techniques
DataFunSummit
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.

Anomaly DetectionInformation Securityanti-fraud
0 likes · 16 min read
Comprehensive Guide to Malicious Website Anti‑Fraud: Detection, Operation, and Modeling
DataFunSummit
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.

Cold StartKuaishoubias correction
0 likes · 19 min read
Cold-Start Content Recommendation Practices at Kuaishou
Alimama Tech
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.

contrastive learninge-commercegraph neural network
0 likes · 13 min read
BOMGraph: Boosting Multi-Scenario E-commerce Search with a Unified Graph Neural Network
DataFunTalk
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.

Cold StartI2UKuaishou
0 likes · 18 min read
Kuaishou Content Cold-Start Recommendation: Challenges, Modeling Solutions, and Future Directions
AntTech
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.

Document UnderstandingLayoutGCNgraph neural network
0 likes · 24 min read
LayoutGCN: A Lightweight Graph Convolutional Network for Visually Rich Document Understanding
DataFunSummit
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.

AIMachine Learningcross-border
0 likes · 18 min read
Applying Artificial Intelligence to Cross‑Border Risk Control: Practices and Insights
Alimama Tech
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.

advertisingend-to-end learninggraph neural network
0 likes · 16 min read
End-to-End Inventory Prediction and Contract Allocation for Guaranteed Delivery Advertising
Alimama Tech
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 predictionadvertising
0 likes · 13 min read
Highlights of Alibaba Mama Team Papers Accepted at CIKM 2023
DataFunSummit
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.

KG reasoningRED-GNNgraph neural network
0 likes · 11 min read
Knowledge Graph based Graph Neural Network Reasoning: From KG Background to GNN for KG and KG for GNN
DataFunSummit
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.

AttentionCold StartEmbedding
0 likes · 9 min read
Graph‑Enhanced Node Representation for Cold‑Start Recommendation: Neighbour‑Enhanced YouTubeDNN
Architect
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.

GCNMachine LearningSCGCN
0 likes · 12 min read
Applying Graph Neural Networks for Anti‑Cheat in Activity Scenarios
Ctrip Technology
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.

Anomaly Detectionfraud detectiongraph neural network
0 likes · 12 min read
Graph-Based Unsupervised Model for Detecting Malicious Account Clusters in Registration Risk Control
DataFunTalk
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.

AIClusteringadvertising
0 likes · 15 min read
Event‑Aware Graph Extraction and Adaptive Clustering‑Gain Network for Insurance Creative Recommendation
DataFunSummit
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.

differentiable reasoningfuzzy logicgraph neural network
0 likes · 22 min read
Integrating Knowledge Graphs with Neural Networks: Generative Pre‑Training, Differentiable Reasoning, and Fuzzy Logic Query Embedding