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e-commerce

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535 articles
Page 18 of 27
DataFunTalk
DataFunTalk
Sep 14, 2020 · Artificial Intelligence

New Generation Rank Technology: Search‑Based Interest Model (SIM) and Dynamic Computation Allocation Framework (DCAF) for Alibaba Directed Advertising

This article presents Alibaba's latest ranking innovations for directed e‑commerce advertising, detailing the challenges of long‑term user interest modeling, the Search‑Based Interest Model (SIM) that extends behavior sequences to ten thousand actions, and the Dynamic Computation Allocation Framework (DCAF) that optimizes per‑request compute resources to maximize system revenue.

CTR modelMachine Learningadvertising
0 likes · 29 min read
New Generation Rank Technology: Search‑Based Interest Model (SIM) and Dynamic Computation Allocation Framework (DCAF) for Alibaba Directed Advertising
DataFunTalk
DataFunTalk
Aug 19, 2020 · Artificial Intelligence

Fraudar: Graph-Based Fraud Detection in E‑commerce Transaction Networks

The article presents a comprehensive overview of e‑commerce fraud, especially brush‑order schemes, and introduces the Fraudar algorithm—a graph‑based unsupervised method that leverages bipartite network analysis, global suspiciousness metrics, priority‑tree optimization, and collaborative supervised training to efficiently identify dense fraudulent sub‑graphs.

Fraudarbipartite graphe-commerce
0 likes · 15 min read
Fraudar: Graph-Based Fraud Detection in E‑commerce Transaction Networks
DataFunTalk
DataFunTalk
Aug 8, 2020 · Artificial Intelligence

Knowledge Graph Construction and Applications in Alibaba B2B E‑commerce

This article explains how Alibaba B2B leverages knowledge‑graph technology—from its historical roots in knowledge engineering and expert systems to modern semantic‑web models, extraction pipelines, reasoning methods, storage solutions, and representation learning—to improve search, recommendation, and scene‑based procurement incentives in e‑commerce platforms.

AlibabaGraph Databasee-commerce
0 likes · 31 min read
Knowledge Graph Construction and Applications in Alibaba B2B E‑commerce
DataFunTalk
DataFunTalk
Jun 15, 2020 · Artificial Intelligence

Understanding and Handling Bad Cases in E-commerce Recommendation Systems

The article explores why bad cases occur in e‑commerce recommendation and search pipelines, classifies their types, demonstrates data‑driven analysis methods, and proposes practical online and offline strategies—including rule‑based fixes, model improvements, and iterative feedback loops—to continuously improve user experience and business metrics.

Machine Learningbadcasedata analysis
0 likes · 23 min read
Understanding and Handling Bad Cases in E-commerce Recommendation Systems
DataFunTalk
DataFunTalk
Jun 3, 2020 · Artificial Intelligence

Semantic Retrieval and Product Ranking in JD E‑commerce Search

This article presents JD's e‑commerce search system, detailing the semantic vector retrieval and product ranking pipelines, the two‑tower deep learning architecture, attention‑based personalization, negative sampling strategies, training optimizations, and real‑world performance gains achieved in production.

Deep Learninge-commercepersonalization
0 likes · 11 min read
Semantic Retrieval and Product Ranking in JD E‑commerce Search
DataFunTalk
DataFunTalk
May 13, 2020 · Artificial Intelligence

Designing and Scaling Recommendation Systems for Cross‑border E‑commerce Growth

This article shares the author’s experience at Club Factory, describing the business model, growth challenges, macro‑ and micro‑level analysis, and detailed technical breakdowns of recommendation system components—including recall, ranking, user interest modeling, evaluation metrics, and ecosystem considerations—to guide scalable e‑commerce growth.

Machine Learningdata-drivene-commerce
0 likes · 17 min read
Designing and Scaling Recommendation Systems for Cross‑border E‑commerce Growth
DataFunTalk
DataFunTalk
Mar 23, 2020 · Artificial Intelligence

Deep Learning Applications in Alibaba 1688 B2B E‑commerce Recommendation System: From Deep Match to Live Content Ranking

This article details how Alibaba's 1688 B2B platform leverages deep learning techniques—including Deep Match, DIN, DIEN, DMR, and heterogeneous network models—to evolve its product recall, ranking, and live‑content recommendation pipelines, highlighting system architecture, practical lessons, and online performance improvements.

AlibabaDeep Learninge-commerce
0 likes · 14 min read
Deep Learning Applications in Alibaba 1688 B2B E‑commerce Recommendation System: From Deep Match to Live Content Ranking
DataFunTalk
DataFunTalk
Feb 26, 2020 · Artificial Intelligence

User Growth, Full‑Stack Growth System, and Deep Learning Applications in Search at Alibaba 1688

This article presents a comprehensive overview of Alibaba 1688’s user‑growth strategy, full‑link growth system, intelligent coupon and push mechanisms, and the application of deep‑learning and optimization techniques in search and order‑aggregation, illustrating how data‑driven algorithms drive e‑commerce performance.

AIAlibabaMachine Learning
0 likes · 20 min read
User Growth, Full‑Stack Growth System, and Deep Learning Applications in Search at Alibaba 1688
DataFunTalk
DataFunTalk
Jan 14, 2020 · Artificial Intelligence

Intelligent Supply‑Demand Matching, Assistants, and Content Operations on Alibaba's 1688 B2B Platform

The article explains how Alibaba's 1688 B2B platform uses AI‑driven supply‑demand matching, intelligent assistants, and automated content generation to streamline massive product operations, improve market analysis, and boost conversion rates, illustrating the underlying data‑driven models and workflow architecture.

AIContent generationIntelligent Assistant
0 likes · 8 min read
Intelligent Supply‑Demand Matching, Assistants, and Content Operations on Alibaba's 1688 B2B Platform
DataFunTalk
DataFunTalk
Sep 20, 2019 · Artificial Intelligence

Multi‑Task Learning for Joint CTR, CVR, and GMV Prediction in E‑commerce

This article describes how a multi‑task learning framework based on ESMM and attention‑shared embeddings was built to jointly predict click‑through rate, conversion rate, and gross merchandise value in a large e‑commerce platform, addressing data sparsity, bias, and training challenges.

CVRESMMattention
0 likes · 8 min read
Multi‑Task Learning for Joint CTR, CVR, and GMV Prediction in E‑commerce
DataFunTalk
DataFunTalk
Aug 23, 2019 · Artificial Intelligence

Club Factory Recommendation System: Overview, Challenges, Architecture, and Ranking Strategies

This article presents a comprehensive overview of Club Factory's recommendation system, covering its product recommendations, key challenges such as user and item cold‑start, the modular architecture, detailed recall and ranking processes, and practical considerations for deployment in e‑commerce.

AIArchitecturee-commerce
0 likes · 3 min read
Club Factory Recommendation System: Overview, Challenges, Architecture, and Ranking Strategies
DataFunTalk
DataFunTalk
Aug 14, 2019 · Artificial Intelligence

Understanding Recommendation Systems: From Information Overload to Personalized AI Solutions

The article explores how the rapid growth of the internet has created information overload, discusses the challenges of recommendation systems such as sparsity and timeliness, outlines a four‑step personalized content pipeline, and highlights the interdisciplinary nature of building effective AI‑driven recommendation solutions.

AIBig DataData Engineering
0 likes · 16 min read
Understanding Recommendation Systems: From Information Overload to Personalized AI Solutions
DataFunTalk
DataFunTalk
Aug 8, 2019 · Artificial Intelligence

Practical Experience of JD E‑commerce Recommendation System: Architecture, Ranking, Real‑time Updates, and Experiment Platform

This article shares JD's e‑commerce recommendation system practice, covering the overall online/offline architecture, recall and ranking modules, real‑time feature and model updates, multi‑objective and diversity strategies, first‑stage index‑based ranking, KNN recall, and a layered experiment platform for rapid iteration.

Machine LearningReal-timee-commerce
0 likes · 14 min read
Practical Experience of JD E‑commerce Recommendation System: Architecture, Ranking, Real‑time Updates, and Experiment Platform
DataFunTalk
DataFunTalk
Jul 25, 2019 · Artificial Intelligence

Understanding Recommendation Ranking: Definition, Decomposition, and Modeling

This article introduces recommendation ranking, breaks down its components, explains how to build ranking models, and provides additional insights, while also presenting the author's background and related job opportunities in the e‑commerce recommendation field.

AIMachine Learninge-commerce
0 likes · 3 min read
Understanding Recommendation Ranking: Definition, Decomposition, and Modeling
DataFunTalk
DataFunTalk
Jul 19, 2019 · Artificial Intelligence

From the Pre‑Recommendation Era to the Bronze Age: Evolution of Recommendation Systems and Mitigating the Matthew Effect

The article traces the historical development of recommendation systems from early manual and hot‑ranking methods through natural ranking and machine‑learning‑based scoring, discusses the Matthew effect and its mitigation via randomization, multi‑objective weighting, and pipeline architectures, and outlines modern personalization and recall strategies for e‑commerce platforms.

DataMachine Learningalgorithms
0 likes · 25 min read
From the Pre‑Recommendation Era to the Bronze Age: Evolution of Recommendation Systems and Mitigating the Matthew Effect
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.

AlibabaB2B2CMachine Learning
0 likes · 17 min read
Alibaba Retail's Intelligent Recommendation System: Business Background, Architecture, Matching and Ranking Models
DataFunTalk
DataFunTalk
Jun 13, 2019 · Artificial Intelligence

What Makes a Good Recommendation System?

This article explores the multifaceted criteria for evaluating a good recommendation system, covering macro and micro perspectives, product domain considerations, information retrieval, algorithmic accuracy, user experience, and business impact, and outlines a systematic iteration process for continuous improvement.

AIUser Experiencealgorithm
0 likes · 13 min read
What Makes a Good Recommendation System?
DataFunTalk
DataFunTalk
May 20, 2019 · Artificial Intelligence

Evolution of Alibaba's Advertising CTR Prediction Models: From Linear Methods to Deep Interest Evolution Networks

The article reviews the characteristics of e‑commerce personalized prediction, outlines Alibaba's model iteration from large‑scale linear regression to deep learning architectures such as DIN, CrossMedia, and Deep Interest Evolution, and discusses future directions like disentangled representation and white‑box modeling.

CTR predictionDeep LearningRecommendation Systems
0 likes · 11 min read
Evolution of Alibaba's Advertising CTR Prediction Models: From Linear Methods to Deep Interest Evolution Networks
DataFunTalk
DataFunTalk
Oct 12, 2018 · Artificial Intelligence

Market Mechanisms and Control Measures in Ele.me Food Delivery Recommendation Algorithms

The article presents a comprehensive overview of Ele.me's food‑delivery recommendation system, detailing its business model, platform goals, unique challenges, market‑driven efficiency mechanisms, control strategies, system architecture, model evolution, and online‑learning techniques used to balance short‑term performance with long‑term ecosystem health.

AIEle.meRecommendation Systems
0 likes · 15 min read
Market Mechanisms and Control Measures in Ele.me Food Delivery Recommendation Algorithms