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Baidu Geek Talk
Baidu Geek Talk
Dec 18, 2024 · Artificial Intelligence

GEE Graph Embedding Algorithm for Business Security Anomaly Detection

The article presents the GEE (Graph Encoder Embedding) algorithm for business security anomaly detection, explains its label‑propagation foundation, evaluates it on ten‑million‑edge real data, identifies inefficiencies in the original implementation, and demonstrates that vectorized NumPy/Pandas optimizations reduce runtime from 55 seconds to about 4 seconds while preserving meaningful TSNE‑visualized embeddings.

GEE algorithmanomaly detectionanti-fraud
0 likes · 21 min read
GEE Graph Embedding Algorithm for Business Security Anomaly Detection
Tencent Cloud Developer
Tencent Cloud Developer
May 29, 2024 · Artificial Intelligence

Distributed Network Embedding Algorithm for Billion‑Scale Graph Data in Tencent Games

Tencent’s Game Social Algorithm Team presents a Spark‑based distributed network embedding framework that recursively partitions hundred‑billion‑edge game graphs into manageable subgraphs, runs node2vec locally, and fuses results, enabling efficient link prediction and node classification across multiple games within hours.

Game AnalyticsSparkdistributed computing
0 likes · 7 min read
Distributed Network Embedding Algorithm for Billion‑Scale Graph Data in Tencent Games
DataFunTalk
DataFunTalk
Jan 11, 2024 · Artificial Intelligence

Graph Models in Baidu Recommendation System: Background, Algorithms, and Evolution

This article introduces the use of graph models in Baidu's recommendation system, covering graph fundamentals, common graph algorithms such as graph embedding and graph neural networks, the evolution of the Feed graph model, and its subsequent promotion across multiple product lines.

Baidugraph embeddinggraph models
0 likes · 10 min read
Graph Models in Baidu Recommendation System: Background, Algorithms, and Evolution
Architect
Architect
Jun 10, 2023 · Artificial Intelligence

An Overview of Twitter’s Open‑Source Recommendation System Architecture

Twitter’s recently open‑sourced recommendation system is dissected, covering its overall architecture, graph‑based data and feature engineering, recall pipelines (in‑network and out‑of‑network), coarse and fine ranking models, mixing and re‑ranking stages, as well as the supporting infrastructure and code examples.

Ranking ModelsTwittergraph embedding
0 likes · 16 min read
An Overview of Twitter’s Open‑Source Recommendation System Architecture
Tongcheng Travel Technology Center
Tongcheng Travel Technology Center
May 9, 2023 · Artificial Intelligence

Enhanced Graph Embedding with Side Information (EGES) for User Growth and Cold‑Start Mitigation

This article presents EGES, a graph‑embedding model that incorporates side information to construct a directed user graph, apply biased random‑walk sampling, and train weighted Skip‑Gram embeddings, thereby improving large‑scale user acquisition and addressing cold‑start challenges in recommendation systems.

EGEScold startgraph embedding
0 likes · 9 min read
Enhanced Graph Embedding with Side Information (EGES) for User Growth and Cold‑Start Mitigation
DataFunSummit
DataFunSummit
Mar 16, 2023 · Artificial Intelligence

Construction of Real‑World Medical Knowledge Graphs and Clinical Event Graphs

The article describes how YiduCloud builds real‑world medical knowledge graphs and clinical event graphs from heterogeneous hospital systems (EMR, HIS, LIS, RIS) using data aggregation, de‑identification, quality control, NLP‑driven entity extraction, standardisation, graph construction, cleaning, embedding and various AI‑powered applications such as decision support, intelligent diagnosis, automated medical‑record generation and patient recruitment.

AIBig DataMedical Knowledge Graph
0 likes · 21 min read
Construction of Real‑World Medical Knowledge Graphs and Clinical Event Graphs
DataFunTalk
DataFunTalk
Oct 3, 2022 · Artificial Intelligence

Building Real‑World Medical Knowledge Graphs and Clinical Event Graphs: Methods, Pipelines, and Applications

This article explains how YiduCore processes heterogeneous hospital data (EMR, HIS, LIS, RIS, literature) to construct real‑world medical knowledge graphs and clinical event graphs, detailing pipelines for entity extraction, normalization, graph cleaning, PSR scoring, graph embedding, and showcasing applications such as intelligent diagnosis, question answering, automated medical record generation, and clinical trial patient recruitment.

AIBig DataMedical Knowledge Graph
0 likes · 21 min read
Building Real‑World Medical Knowledge Graphs and Clinical Event Graphs: Methods, Pipelines, and Applications
DataFunSummit
DataFunSummit
Jul 10, 2022 · Artificial Intelligence

Intelligent Industry Analysis Tool Based on Knowledge Graphs and Industry Atoms

This article introduces VentureSights, an AI‑driven intelligent industry analysis platform built on knowledge‑graph technology and the concept of industry atoms, detailing its core modules, workflow, industry‑atom representation, extraction algorithms, and overall system architecture for generating comprehensive industry reports and insights.

Industry analysisKnowledge Graphartificial intelligence
0 likes · 12 min read
Intelligent Industry Analysis Tool Based on Knowledge Graphs and Industry Atoms
Alimama Tech
Alimama Tech
Jul 6, 2022 · Artificial Intelligence

How Mixed‑Curvature Graph Embeddings Boost E‑commerce Ad Retrieval

This article presents AMCAD, an adaptive mixed‑curvature graph embedding system that models heterogeneous e‑commerce search ad graphs in non‑Euclidean spaces, detailing its sample construction, three‑stage model architecture, offline and online experiments, and demonstrating significant performance gains over Euclidean baselines.

Deep Learningadvertisement retrievale‑commerce
0 likes · 13 min read
How Mixed‑Curvature Graph Embeddings Boost E‑commerce Ad Retrieval
HelloTech
HelloTech
May 26, 2022 · Artificial Intelligence

Hello's Automated Growth Algorithm Loop: C‑Side Scenarios, Challenges, and Active Growth Strategies

Hello’s automated C‑side growth algorithm loop integrates diverse traffic sources, semi‑supervised PU‑learning, graph‑based look‑alike targeting, causal uplift models for smart subsidies, and adaptive copy and external ad optimization, dramatically boosting ride‑hailing and lifestyle service revenue while minimizing engineering duplication.

AI PlatformRecommendation SystemsUplift Modeling
0 likes · 20 min read
Hello's Automated Growth Algorithm Loop: C‑Side Scenarios, Challenges, and Active Growth Strategies
DataFunSummit
DataFunSummit
Apr 17, 2022 · Artificial Intelligence

Precise Marketing Algorithms and Practices at Hello Mobility

This article presents Hello Mobility’s precise marketing system, covering its business background, value, framework, algorithmic capabilities such as Pu‑Learning LookAlike modeling, TSA semi‑supervised learning, and Graph Embedding, as well as identified pain points, project impact, and future directions for scaling and automation.

AISemi-supervised Learninggraph embedding
0 likes · 12 min read
Precise Marketing Algorithms and Practices at Hello Mobility
DataFunTalk
DataFunTalk
Apr 11, 2022 · Artificial Intelligence

Precise Marketing Algorithms and Practices at Hello Mobility

This article presents Hello Mobility's precise marketing system, detailing its business background, value, framework, algorithmic capabilities—including Pu‑Learning LookAlike modeling, semi‑supervised TSA, and graph‑embedding techniques—addressing challenges such as sparse features and low ROI, and sharing performance improvements and future directions.

Pu-LearningROIUser Segmentation
0 likes · 14 min read
Precise Marketing Algorithms and Practices at Hello Mobility
ByteDance SE Lab
ByteDance SE Lab
Oct 29, 2021 · Artificial Intelligence

What Is a Knowledge Graph? From Basics to Embedding Techniques

This article introduces knowledge graphs, defining them as semantic networks or multi‑relational graphs, explains entities and relations, compares RDF and graph‑database storage, outlines construction steps including entity extraction and ontology building, reviews embedding models like TransE/H/R/D, and explores applications in search, finance, recommendation, and language models.

AIKnowledge Graphgraph embedding
0 likes · 22 min read
What Is a Knowledge Graph? From Basics to Embedding Techniques
Baidu Intelligent Testing
Baidu Intelligent Testing
Sep 14, 2021 · Information Security

Community Encoding Based Detection of Black and Gray Market Attacks Using Graph Embedding

This article presents a community‑encoding approach that leverages large‑scale graph‑embedding (GraphSAGE) and asynchronous near‑real‑time engineering to identify and measure unknown black‑gray market attacks with higher accuracy and flexibility than traditional graph‑mining methods.

GraphSAGEUnsupervised Learningblack‑gray market
0 likes · 15 min read
Community Encoding Based Detection of Black and Gray Market Attacks Using Graph Embedding
DataFunTalk
DataFunTalk
Jun 23, 2021 · Artificial Intelligence

Graph Algorithm Practices for Anti‑Cheat on the Douyu Live‑Streaming Platform

This article explains how Douyu uses graph‑based algorithms to detect and mitigate fraudulent streaming traffic, covering the platform's risk‑control scenarios, the overall graph architecture, its evolution, modeling workflow, practical case studies, and the resulting improvements in detection accuracy and interpretability.

anti-cheatgraph algorithmsgraph embedding
0 likes · 16 min read
Graph Algorithm Practices for Anti‑Cheat on the Douyu Live‑Streaming Platform
Baidu Geek Talk
Baidu Geek Talk
Jun 23, 2021 · Information Security

Black-Gray Industry Attack Detection Based on Community Encoding Using Graph Embedding

The paper introduces a community‑encoding, GraphSAGE‑based detection framework that embeds whole user‑account, IP, device, and phone‑number graphs—both homogeneous and heterogeneous—to identify previously unseen black‑gray industry attacks, achieving about 95% IP‑risk accuracy via an asynchronous near‑real‑time system, though computational and automation challenges persist.

GraphSAGEblack-gray-industrycommunity-detection
0 likes · 12 min read
Black-Gray Industry Attack Detection Based on Community Encoding Using Graph Embedding
Tencent Cloud Developer
Tencent Cloud Developer
Jun 9, 2021 · Artificial Intelligence

Overview of Common Graph Embedding Methods in Industry

The article surveys six widely‑used graph‑embedding techniques—DeepWalk, Node2Vec, LINE, SDNE, EGES and Metapath2Vec—explaining how each transforms graph topology into low‑dimensional vectors via random walks, biased sampling, proximity‑based objectives, deep auto‑encoders, side‑information integration, or meta‑path‑guided walks for industrial applications.

DeepWalkEGESMetaPath2Vec
0 likes · 14 min read
Overview of Common Graph Embedding Methods in Industry
DataFunTalk
DataFunTalk
Jan 1, 2021 · Artificial Intelligence

Hot Topic Mining and Expansion Using User‑Behavior Graph Embedding for Recommendation Systems

This article surveys recent research on extracting and expanding hot topics from short texts by constructing user‑behavior graphs, applying graph‑embedding techniques, and leveraging multi‑task learning to improve recommendation relevance, timeliness, and cold‑start handling in large‑scale platforms.

Knowledge GraphRecommendation Systemsartificial intelligence
0 likes · 12 min read
Hot Topic Mining and Expansion Using User‑Behavior Graph Embedding for Recommendation Systems
Meituan Technology Team
Meituan Technology Team
Dec 3, 2020 · Artificial Intelligence

Meituan Knowledge Graph Group's Six Papers Accepted at CIKM 2020

Meituan’s search and NLP team announced that six knowledge‑graph papers—covering query‑aware tip generation, BERT‑based ranking, multi‑modal and sequential recommendation, conversational recommendation, and graph‑embedding for personalized product search—were accepted at CIKM 2020, resulting from university collaborations and already deployed to boost Meituan’s search, recommendation and product‑search services.

BERTCIKM 2020Knowledge Graph
0 likes · 13 min read
Meituan Knowledge Graph Group's Six Papers Accepted at CIKM 2020
JD Retail Technology
JD Retail Technology
Oct 21, 2020 · Artificial Intelligence

Galileo: A Distributed Graph Deep Learning Framework for Large‑Scale Industrial Scenarios

The article introduces Galileo, JD Retail's distributed graph deep‑learning platform that supports heterogeneous and dynamic graphs, ultra‑large scale training, flexible model customization, and seamless integration with TensorFlow and PyTorch, highlighting its architecture, core challenges, built‑in algorithms, and upcoming open‑source release.

AI PlatformDistributed Traininggraph embedding
0 likes · 11 min read
Galileo: A Distributed Graph Deep Learning Framework for Large‑Scale Industrial Scenarios
DataFunTalk
DataFunTalk
Oct 18, 2020 · Artificial Intelligence

Unifying Skip‑gram and Matrix Factorization for Graph Embedding and Enhancing It with Sparse Matrix Techniques

This article reviews how skip‑gram‑based graph embedding methods such as DeepWalk, LINE and node2vec can be interpreted as matrix factorization, explains the NetMF and NetSMF frameworks that use sparse matrix approximations and random SVD for large‑scale networks, and discusses extensions like GATNE and deep clustering approaches to address practical challenges in constructing and applying graph representations.

graph embeddinggraph neural networksmatrix factorization
0 likes · 13 min read
Unifying Skip‑gram and Matrix Factorization for Graph Embedding and Enhancing It with Sparse Matrix Techniques
DataFunTalk
DataFunTalk
Oct 17, 2020 · Artificial Intelligence

DyHAN: Dynamic Heterogeneous Graph Embedding with Hierarchical Attention

This article introduces DyHAN, a dynamic heterogeneous graph embedding method that employs hierarchical attention across node, edge, and temporal dimensions to capture evolving user-item interactions, demonstrates superior performance over static and existing dynamic baselines, and reports significant online improvements in Alibaba’s recommendation system.

AlibabaAttention Mechanismdynamic graphs
0 likes · 9 min read
DyHAN: Dynamic Heterogeneous Graph Embedding with Hierarchical Attention
DataFunTalk
DataFunTalk
Aug 23, 2020 · Artificial Intelligence

Optimizing Recall in Travel Recommendation Systems: Challenges and Solutions at Alibaba's Fliggy

This article explains how Fliggy's travel recommendation platform tackles recall challenges such as cold‑start users, sparse behavior, itinerary‑specific needs, and periodic repurchase by applying user‑attribute models, graph embeddings, dual‑tower architectures, session‑based methods, and statistical repurchase forecasting to improve candidate selection and overall recommendation performance.

Travelcold startgraph embedding
0 likes · 16 min read
Optimizing Recall in Travel Recommendation Systems: Challenges and Solutions at Alibaba's Fliggy
MaGe Linux Operations
MaGe Linux Operations
Aug 18, 2020 · Artificial Intelligence

Understanding node2vec: Biased Random Walks for Graph Embedding

This article explains the node2vec algorithm, its mathematical foundations, biased random‑walk sampling strategy with parameters p and q, implementation details using the Alias method, and demonstrates its superior performance on node classification and visualization tasks compared with DeepWalk and LINE.

Pythongraph embeddingmachine learning
0 likes · 9 min read
Understanding node2vec: Biased Random Walks for Graph Embedding
Sohu Tech Products
Sohu Tech Products
May 27, 2020 · Artificial Intelligence

Overview of Graph Embedding Techniques: DeepWalk, LINE, node2vec, and EGES

This article provides a comprehensive overview of graph embedding methods—including DeepWalk, LINE, node2vec, and EGES—explaining their algorithms, random‑walk strategies, proximity definitions, incorporation of side information, and their applications in large‑scale recommendation systems.

DeepWalkRecommendation Systemsgraph embedding
0 likes · 20 min read
Overview of Graph Embedding Techniques: DeepWalk, LINE, node2vec, and EGES
Alibaba Cloud Developer
Alibaba Cloud Developer
May 7, 2020 · Artificial Intelligence

How Hierarchical Attention Boosts Dynamic Heterogeneous Graph Embedding

This article introduces DyHAN, a hierarchical attention‑based dynamic heterogeneous graph embedding method that captures temporal, node‑level, and edge‑level information, demonstrates superior offline and online performance on Alibaba’s ICBU recommendation system, and discusses dataset construction, model architecture, and future challenges.

DyHANHierarchical Attentiondynamic heterogeneous graph
0 likes · 10 min read
How Hierarchical Attention Boosts Dynamic Heterogeneous Graph Embedding
DataFunTalk
DataFunTalk
Apr 27, 2020 · Artificial Intelligence

Graph-Based Recommendation Algorithms and Cold‑Start Solutions

This article presents a comprehensive overview of graph‑based recommendation techniques, including collaborative filtering, graph embedding, side‑information enhanced embeddings, two‑tower DSSM models, and practical cold‑start strategies from Alibaba and Airbnb, followed by a mixed model and Q&A session.

AIRecommendation Systemscold start
0 likes · 14 min read
Graph-Based Recommendation Algorithms and Cold‑Start Solutions
DataFunTalk
DataFunTalk
Mar 4, 2020 · Artificial Intelligence

Building and Applying Relationship Graphs at Beike Real Estate: Architecture, Embedding, and Recommendation

The talk explains how Beike Real Estate constructs a large‑scale relationship graph from billions of user, house, and agent interactions, quantifies edge strengths, builds homogeneous and heterogeneous sub‑graphs, derives graph capabilities such as node influence, embedding, similarity and relation prediction, and finally deploys these capabilities in multi‑degree queries, house‑similarity recommendations and B‑side agent assistance, achieving measurable CTR improvements.

AIKnowledge Graphgraph embedding
0 likes · 17 min read
Building and Applying Relationship Graphs at Beike Real Estate: Architecture, Embedding, and Recommendation
DataFunTalk
DataFunTalk
Dec 24, 2019 · Artificial Intelligence

Evolution of Recall Models in Recommendation Systems: From Collaborative Filtering to Deep Learning and Tree‑Based Retrieval

This article surveys the development of recall modules in large‑scale recommendation systems, covering traditional item‑based collaborative filtering, single‑embedding DNN and dual‑tower approaches, multi‑interest capsule networks, graph‑based embeddings, long‑short term interest modeling, and the tree‑structured TDM framework for efficient deep matching.

Deep LearningRecommendation Systemsgraph embedding
0 likes · 14 min read
Evolution of Recall Models in Recommendation Systems: From Collaborative Filtering to Deep Learning and Tree‑Based Retrieval
Didi Tech
Didi Tech
Dec 2, 2019 · Artificial Intelligence

Reinforcement Learning for Intelligent Marketing in Didi's Xiaoju Car Service

Didi’s Xiaoju Car Service leverages a reinforcement‑learning framework with Double DQN and graph‑embedding‑based personalization across its traffic‑distribution, tagging, portrait, targeting, strategy, and reach‑optimization modules, replacing manual rule‑based marketing, and achieves roughly 8 % new‑user lift, 50 % cost reduction, and significant gains in open and conversion rates.

Didigraph embeddingintelligent marketing
0 likes · 12 min read
Reinforcement Learning for Intelligent Marketing in Didi's Xiaoju Car Service
DataFunTalk
DataFunTalk
Nov 27, 2019 · Artificial Intelligence

Applying Reinforcement Learning and Graph Embedding for Intelligent User Operations in Didi Ride‑Sharing

This article describes how Didi Ride‑Sharing leverages reinforcement learning and graph‑embedding techniques to model and optimize user‑operation marketing, detailing system architecture, algorithm design, experimental ROI improvements, and personalized message delivery for enhanced conversion and cost efficiency.

DidiROIgraph embedding
0 likes · 11 min read
Applying Reinforcement Learning and Graph Embedding for Intelligent User Operations in Didi Ride‑Sharing
DataFunTalk
DataFunTalk
Oct 14, 2019 · Artificial Intelligence

Advances in Short Video Recommendation: Multi‑Objective Optimization and Model Enhancements

This article presents a comprehensive overview of short‑video recommendation at UC, covering business background, system architecture, the evolution from LR to Wide & Deep models, multi‑objective loss design with positive‑sample weighting, graph‑embedding fusion, time‑weighted loss, continuity modeling, a Boosting‑based WnD solution, and future research directions.

Deep Learningboostinggraph embedding
0 likes · 11 min read
Advances in Short Video Recommendation: Multi‑Objective Optimization and Model Enhancements
DataFunTalk
DataFunTalk
Jul 11, 2019 · Artificial Intelligence

Alibaba Retail's Intelligent Recommendation System: Business Background, Architecture, Matching and Ranking Models

This article presents a comprehensive overview of Alibaba Retail's B2B2C intelligent recommendation platform, detailing its business context, three core recommendation scenarios, system architecture, matching algorithms such as item‑CF, graph embedding and user‑CF, as well as the evolution of ranking models and feature engineering practices.

AlibabaB2B2Ce‑commerce
0 likes · 17 min read
Alibaba Retail's Intelligent Recommendation System: Business Background, Architecture, Matching and Ranking Models
DataFunTalk
DataFunTalk
Apr 28, 2019 · Artificial Intelligence

Graph Algorithms for Fraud Detection and Community Detection: Modularity, Louvain, Infomap, node2vec and comE

This article explains how graph‑based algorithms such as centrality measures, modularity optimization, Louvain, Infomap, node2vec and the comE framework can be applied to financial fraud detection and community discovery, detailing their principles, formulas, implementation steps and evaluation metrics.

Infomapcommunity-detectionfraud detection
0 likes · 14 min read
Graph Algorithms for Fraud Detection and Community Detection: Modularity, Louvain, Infomap, node2vec and comE
Alibaba Cloud Developer
Alibaba Cloud Developer
Apr 1, 2019 · Artificial Intelligence

How Alibaba’s Knowledge Engine Advances AI with Adversarial NER and Graph Embedding

This article reviews Alibaba’s year‑long Knowledge Engine program, detailing its five‑module architecture, major technical breakthroughs such as automatic ontology building and deep‑learning alignment, and two flagship research works: adversarial learning for crowdsourced NER and an iterative rule‑and‑embedding reasoning framework.

AIKnowledge Graphadversarial learning
0 likes · 9 min read
How Alibaba’s Knowledge Engine Advances AI with Adversarial NER and Graph Embedding
Alibaba Cloud Developer
Alibaba Cloud Developer
Dec 5, 2018 · Artificial Intelligence

How AI Optimizes E‑Commerce ‘Bundle‑Buy’ with Graph Embedding & Knapsack

This article explains how Alibaba's search team leverages AI techniques such as graph embedding, scenario‑based recommendation, and a multiple‑choice knapsack model to intelligently select complementary items during the Double Eleven shopping festival, balancing price constraints, user experience, and conversion efficiency.

Recommendation Systemse‑commercegraph embedding
0 likes · 15 min read
How AI Optimizes E‑Commerce ‘Bundle‑Buy’ with Graph Embedding & Knapsack
Alibaba Cloud Developer
Alibaba Cloud Developer
Oct 10, 2018 · Artificial Intelligence

How Virtual Category Trees Boost E‑Commerce Search and Recommendation

This article explains how Alibaba builds a virtual category (CPV) system for Taobao by merging similar categories, splitting overly coarse ones, and constructing a hierarchical virtual category tree using methods such as PMI, title term similarity, frequent itemset mining, and graph‑embedding techniques, ultimately reducing user fatigue and improving CTR.

category mergingcategory splittinggraph embedding
0 likes · 16 min read
How Virtual Category Trees Boost E‑Commerce Search and Recommendation
Alibaba Cloud Developer
Alibaba Cloud Developer
Aug 29, 2018 · Artificial Intelligence

How Graph Embedding Boosts E‑Commerce Recommendations: GES & EGES Explained

An in‑depth look at Alibaba’s billion‑scale graph embedding framework—GES and EGES—reveals how side‑information‑enhanced embeddings address user long‑tail coverage and cold‑start challenges, improving recommendation diversity and discovery across massive e‑commerce datasets and enabling real‑time personalized ranking.

Recommendation Systemscold starte‑commerce
0 likes · 7 min read
How Graph Embedding Boosts E‑Commerce Recommendations: GES & EGES Explained
Alibaba Cloud Developer
Alibaba Cloud Developer
Feb 1, 2018 · Artificial Intelligence

How Alibaba’s Graph‑Based Bundle Mining Doubles Conversion in E‑Commerce

Alibaba’s latest bundle‑mining system leverages weighted graph embedding and real‑time sampling to recommend complementary products, replacing traditional item‑to‑item similarity, boosting click‑through rates by up to 13% offline and 4% online during the Double‑11 promotion while handling billions of edges.

Real-time Processingbundle mininge‑commerce
0 likes · 12 min read
How Alibaba’s Graph‑Based Bundle Mining Doubles Conversion in E‑Commerce
Alibaba Cloud Developer
Alibaba Cloud Developer
Dec 20, 2017 · Artificial Intelligence

How Alibaba Leverages Graph Embedding & Deep Learning for Double 11 Home‑Page Recommendations

This article explains how Alibaba's recommendation team built a large‑scale, AI‑driven personalization pipeline for the Double 11 shopping festival, using graph‑embedding recall, deep‑learning ranking models such as DeepResNet, DCN, and a custom XTensorflow platform to improve coverage, diversity, and click‑through rates.

AIDeep Learninge‑commerce
0 likes · 20 min read
How Alibaba Leverages Graph Embedding & Deep Learning for Double 11 Home‑Page Recommendations
Alibaba Cloud Developer
Alibaba Cloud Developer
Feb 20, 2017 · Artificial Intelligence

How Alibaba’s Graph Embedding Boosts E‑Commerce Recommendations by 60%

Alibaba’s merchant division introduced a scalable graph‑embedding approach for its “thousands‑of‑people‑one‑face” recommendation module, enabling personalized product suggestions within sparse shop data, improving click‑through rates by 30% and conversions by 60%, and presenting theoretical insights validated at AAAI 2017.

e‑commercegraph embeddingmachine learning
0 likes · 13 min read
How Alibaba’s Graph Embedding Boosts E‑Commerce Recommendations by 60%