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DataFunTalk

Jun 14, 2024 · Artificial Intelligence

Shopee's E‑commerce Knowledge Graph Construction and Integration with Large Models

This article presents Shopee's comprehensive exploration of building an e‑commerce knowledge graph, detailing its challenges, construction pipeline, AI‑driven extraction and fusion techniques, multilingual and multimodal modeling, and practical applications ranging from search and recommendation to AI assistants and real‑time updates.

AI ApplicationsInformation Extractione-commerce

0 likes · 21 min read

Shopee's E‑commerce Knowledge Graph Construction and Integration with Large Models

DataFunTalk

Mar 18, 2024 · Artificial Intelligence

High-Fidelity Image-to-Video Generation for E‑commerce Product Motion with AtomoVideo and Noise Rectification

This article presents Alibaba's research on using diffusion‑based AIGC techniques, including a training‑free Noise Rectification module and the AtomoVideo model, to automatically convert static product images into high‑quality, detail‑preserving video motions for e‑commerce advertising.

AIGCAtomoVideoDiffusion Model

0 likes · 15 min read

High-Fidelity Image-to-Video Generation for E‑commerce Product Motion with AtomoVideo and Noise Rectification

DataFunTalk

Jan 16, 2024 · Artificial Intelligence

Applying Knowledge Graphs to E‑commerce AIGC: From Domain‑Specific to General Knowledge Graphs and LLM Integration

This article presents a comprehensive overview of how knowledge graphs are leveraged in e‑commerce AIGC pipelines, detailing domain‑specific and general graph‑based text generation, model architecture, controllable generation techniques, experimental results, and future directions for large language model integration.

AIGCLarge Language Modeldomain adaptation

0 likes · 22 min read

Applying Knowledge Graphs to E‑commerce AIGC: From Domain‑Specific to General Knowledge Graphs and LLM Integration

DataFunTalk

Aug 28, 2023 · Big Data

Practical Experience of an E‑commerce Platform’s Offline and Real‑time Data Warehouse

This article shares the practical architecture, technology selection, implementation details, and evolution of an e‑commerce platform’s offline and real‑time data warehouses, covering data modeling, processing pipelines, system components such as Hive, Spark, Flink, ClickHouse, Doris, and Hudi, and the lessons learned from multiple production deployments.

Big DataClickHouseData Warehouse

0 likes · 18 min read

Practical Experience of an E‑commerce Platform’s Offline and Real‑time Data Warehouse

DataFunTalk

Jul 12, 2023 · Artificial Intelligence

Evolution of Search EE System: Adaptive Exploration, Scenario Modeling, End-to-End Scoring Consistency, and Context-Aware Brand Store Detection

This article outlines the recent full‑cycle iterations of JD’s search Explore‑Exploit (EE) system, covering adaptive dynamic detection models, upgraded scenario modeling, two‑stage scoring and insertion consistency, end‑to‑end dynamic insertion, and context‑aware brand‑store dimension detection, with detailed methodology, experiments, and online results.

Machine Learninge-commerceexplore‑exploit

0 likes · 22 min read

DataFunTalk

Jun 28, 2023 · Artificial Intelligence

Building and Applying a Multi‑Language Product Knowledge Graph at Shopee

This presentation details Shopee's approach to constructing a multilingual product knowledge graph, covering ontology modeling, data acquisition, fusion techniques, and practical applications, while discussing challenges, model architectures, and future directions for large‑scale e‑commerce AI systems.

Machine Learninge-commerceknowledge graph

0 likes · 20 min read

Building and Applying a Multi‑Language Product Knowledge Graph at Shopee

DataFunTalk

Apr 27, 2023 · Big Data

How to Build an E‑commerce Data Metric System

This article explains the concepts of good data metrics, how to identify and select appropriate indicators, and provides a step‑by‑step methodology—including the OSM model and a practical e‑commerce case study—for building a comprehensive data metric system that drives business growth.

KPIOSM modeldata analysis

0 likes · 15 min read

How to Build an E‑commerce Data Metric System

DataFunTalk

Jan 29, 2023 · Artificial Intelligence

Data Science Practices in E‑commerce Search: Experimentation, Causal Inference, and Metric Design

This article presents the JD Retail search data‑science team's practical approaches to e‑commerce search, covering the scene’s unique data characteristics, order attribution methods, AB experiment design, causal‑inference frameworks, variance‑reduction techniques, quasi‑experimental evaluations, and metric design for traffic distribution, all illustrated with real‑world examples and visualizations.

AB testingcausal inferencedata science

0 likes · 18 min read

Data Science Practices in E‑commerce Search: Experimentation, Causal Inference, and Metric Design

DataFunTalk

Dec 7, 2022 · Artificial Intelligence

Entire Space Delayed Feedback with Cross‑Task Knowledge Distillation (ESDC) for Multi‑Task E‑commerce Recommendation

This article presents Xiaomi’s e‑commerce recommendation research, addressing four key challenges—sample selection bias, data sparsity, delayed feedback, and knowledge inconsistency—by introducing the Entire Space Delayed Feedback with Cross‑Task Knowledge Distillation (ESDC) model, which combines causal inference, cross‑task distillation, twin networks, and uncertainty weighting to improve CVR prediction and achieve a 15% GMV lift over the baseline.

AICVRDelayed Feedback

0 likes · 11 min read

Entire Space Delayed Feedback with Cross‑Task Knowledge Distillation (ESDC) for Multi‑Task E‑commerce Recommendation

DataFunTalk

Oct 16, 2022 · Artificial Intelligence

Query Understanding and Processing in E‑commerce Search Systems

This article explains the end‑to‑end pipeline of query understanding for e‑commerce search, covering preprocessing, segmentation, spell correction, normalization, and expansion, and discusses both academic research and industry implementations with examples and references.

Search Enginee-commercenatural language processing

0 likes · 13 min read

Query Understanding and Processing in E‑commerce Search Systems

DataFunTalk

Oct 11, 2022 · Artificial Intelligence

Search vs Recommendation vs Advertising: Concepts, Differences, and System Architectures

This article provides an overview of search, recommendation, and advertising as core internet services, comparing their problem definitions, business goals, algorithmic models, and system architectures across web, e‑commerce, and O2O scenarios, while outlining historical development and key industry examples.

AIadvertisingalgorithm

0 likes · 13 min read

Search vs Recommendation vs Advertising: Concepts, Differences, and System Architectures

DataFunTalk

Sep 2, 2022 · Artificial Intelligence

Applying Reinforcement Learning to E‑commerce Traffic Control: Practices and Future Directions

This talk by JD Retail's Zhao Yu explains how reinforcement learning is modeled and deployed for large‑scale traffic control during major sales events, detailing system architecture, reward design, offline simulation, model upgrades, and future research directions.

JD.comRL modelinge-commerce

0 likes · 20 min read

Applying Reinforcement Learning to E‑commerce Traffic Control: Practices and Future Directions

DataFunTalk

May 31, 2022 · Artificial Intelligence

Intelligent Creative Content Ecosystem for JD Advertising: Content Understanding, Generation, and Distribution

This talk presents JD's intelligent creative ecosystem for advertising, detailing the construction of a content understanding system, AI-driven content generation (including OCR, image tagging, video summarization, and copy generation), and multimodal creative selection and distribution, highlighting challenges, solutions, and business impact.

AIContent UnderstandingMachine Learning

0 likes · 27 min read

Intelligent Creative Content Ecosystem for JD Advertising: Content Understanding, Generation, and Distribution

DataFunTalk

May 25, 2022 · Artificial Intelligence

Optimizing E-commerce Product Copy Generation: Challenges, Framework, and System Practices

This article presents a comprehensive overview of the challenges in e‑commerce product copy generation, introduces a unified framework comprising a copy generation system, a copy‑cleaning subsystem, and a quality evaluation module, and details practical optimization techniques applied to short and long copy scenarios.

AIModel OptimizationTransformer

0 likes · 17 min read

Optimizing E-commerce Product Copy Generation: Challenges, Framework, and System Practices

DataFunTalk

May 7, 2022 · Artificial Intelligence

Intelligent Recommendation Selling Point Generation: Architecture, Core AI Techniques, Model Development, and Product Impact

This article explains how JD's intelligent recommendation selling point system leverages NLP, BERT, Transformer and pointer‑generator models to automatically create short, personalized product highlights, describing the technical background, system architecture, model training pipeline, online/offline monitoring, and the resulting business benefits.

BERTNLPRecommendation Systems

0 likes · 13 min read

Intelligent Recommendation Selling Point Generation: Architecture, Core AI Techniques, Model Development, and Product Impact

DataFunTalk

Apr 6, 2022 · Artificial Intelligence

AIDA Advertising Intelligent Decision and Allocation Framework: Evolution of Smart Auction Mechanisms

This article introduces the AIDA framework for Alibaba's display advertising, detailing the business background, multi‑objective optimization challenges, the design of Deep GSP and Neural Auction mechanisms powered by deep learning and reinforcement learning, and outlines future technical and platform directions while also announcing recruitment opportunities.

AIBig DataDeep Learning

0 likes · 16 min read

AIDA Advertising Intelligent Decision and Allocation Framework: Evolution of Smart Auction Mechanisms

DataFunTalk

Mar 14, 2022 · Artificial Intelligence

Advanced Practices in E‑commerce Recommendation: Multi‑Objective Optimization, User Behavior Sequence Modeling, Fine‑Grained Behavior Modeling, and Multimodal Features

The article presents JD's end‑to‑end recommendation pipeline, detailing the four‑stage ranking chain, challenges of fine‑ranking, and practical solutions including multi‑objective learning, transformer‑based user behavior sequence modeling, fine‑grained click behavior integration, and multimodal image features, with offline and online performance gains.

Recommendation SystemsTransformere-commerce

0 likes · 18 min read

Advanced Practices in E‑commerce Recommendation: Multi‑Objective Optimization, User Behavior Sequence Modeling, Fine‑Grained Behavior Modeling, and Multimodal Features

DataFunTalk

Feb 22, 2022 · Artificial Intelligence

Real‑Time Graph Neural Network for Payment Fraud Detection at eBay

This article describes how eBay applies graph neural networks to real‑time payment fraud detection, covering the anti‑fraud scenario, limitations of traditional GBDT pipelines, challenges of constructing and serving dynamic heterogeneous graphs, the end‑to‑end solution with directed slice graphs and a Lambda‑style architecture, and experimental results comparing GNN with LightGBM.

Graph Neural NetworksMachine Learninge-commerce

0 likes · 15 min read

Real‑Time Graph Neural Network for Payment Fraud Detection at eBay

DataFunTalk

Feb 2, 2022 · Artificial Intelligence

UGC Sentiment Analysis Solutions and Applications in Taobao

This article presents a comprehensive overview of Taobao's user‑generated content sentiment analysis pipeline, covering task definition, challenges, model architecture with RoBERTa‑based extraction, sentiment‑knowledge pre‑training, graph augmentation, personalized ranking, business impact metrics, and future research directions.

Deep LearningUGCe-commerce

0 likes · 16 min read

UGC Sentiment Analysis Solutions and Applications in Taobao

DataFunTalk

Jan 19, 2022 · Artificial Intelligence

ZEUS: A Self‑Supervised Multi‑Scenario Query Ranking Model for E‑commerce Search

The article presents ZEUS, a self‑supervised multi‑scenario ranking model that leverages user‑initiated behavior pre‑training to break feedback loops and improve query recommendation efficiency across diverse e‑commerce search scenarios, achieving significant gains in CTR, CVR, and GMV.

CTR predictionTransformere-commerce

0 likes · 19 min read