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1283 articles · Page 9 of 13
DataFunTalk
DataFunTalk
Oct 31, 2020 · Artificial Intelligence

Trajectory Classification for Road Closure Detection Using Bayesian and Deep Learning Approaches

This article investigates how to dynamically identify road closures by classifying vehicle, bicycle, and pedestrian trajectories, addressing sample imbalance and noisy labels with a label‑probability mixture Bayesian model and a deep‑learning image‑encoding pipeline, and compares their experimental results.

Artificial IntelligenceBayesian modeldeep learning
0 likes · 12 min read
Trajectory Classification for Road Closure Detection Using Bayesian and Deep Learning Approaches
Suning Technology
Suning Technology
Oct 29, 2020 · Artificial Intelligence

Accelerating Deep Learning for Retail: Model Compression, Speed & Energy

This lecture outlines the key challenges of deep learning in retail—growing model size, speed, and energy consumption—and presents a comprehensive acceleration framework covering algorithmic optimizations like network design, pruning, and hardware acceleration, with practical examples such as MobileNet, model compression, and edge deployment.

deep learninghardware optimizationmodel acceleration
0 likes · 15 min read
Accelerating Deep Learning for Retail: Model Compression, Speed & Energy
Tencent Advertising Technology
Tencent Advertising Technology
Oct 29, 2020 · Artificial Intelligence

Large-Scale User Visits Understanding and Forecasting with Deep Spatial-Temporal Tensor Factorization Framework

This article discusses a deep spatial-temporal tensor factorization framework for large-scale user visits understanding and forecasting, addressing challenges in advertising inventory prediction and demonstrating significant improvements over traditional methods.

Artificial IntelligenceData ScienceTime Series Forecasting
0 likes · 9 min read
Large-Scale User Visits Understanding and Forecasting with Deep Spatial-Temporal Tensor Factorization Framework
Beike Product & Technology
Beike Product & Technology
Oct 24, 2020 · Artificial Intelligence

FrameX: An AI System for Intelligent Floorplan Analysis and Applications

FrameX is an AI-powered platform developed by Beike’s Data Intelligence Center that leverages vector floorplan data to automatically tag, score, interpret, cluster, and retrieve housing layouts, supporting numerous business scenarios through a layered architecture of data, feature, and application layers.

AIClusteringFloorplan Analysis
0 likes · 9 min read
FrameX: An AI System for Intelligent Floorplan Analysis and Applications
DataFunTalk
DataFunTalk
Oct 23, 2020 · Artificial Intelligence

Feedback‑Aware Deep Matching Model for Music Recommendation in Tmall Genie

This article presents DeepMatch, a behavior‑sequence based deep learning recall model enhanced with play‑rate and intent‑type embeddings, describes its self‑attention architecture, factorized embedding parameterization, multitask loss design, distributed TensorFlow training tricks, and demonstrates significant offline and online improvements in music recommendation performance.

TensorFlowdeep learningmultitask learning
0 likes · 15 min read
Feedback‑Aware Deep Matching Model for Music Recommendation in Tmall Genie
Didi Tech
Didi Tech
Oct 21, 2020 · Artificial Intelligence

Deep Model Compression Techniques for Intelligent Automotive Cockpits

The article reviews deep‑model compression methods—ADMM‑based structured pruning, low‑bit quantization, and teacher‑student knowledge distillation—and their automated AutoCompress workflow, demonstrating how these techniques shrink neural networks enough to run real‑time driver‑monitoring and other intelligent cockpit functions on resource‑limited automotive hardware while preserving accuracy.

ADMMEdge AIdeep learning
0 likes · 16 min read
Deep Model Compression Techniques for Intelligent Automotive Cockpits
DataFunTalk
DataFunTalk
Oct 20, 2020 · Artificial Intelligence

From Biological Neurons to Artificial Neural Networks: Perceptrons, Multilayer Perceptrons, and Backpropagation

This article traces the evolution of artificial neural networks from their biological inspiration, explains the McCulloch‑Pitts neuron model, details perceptron architecture and learning rule with a Scikit‑Learn example, and introduces multilayer perceptrons and the back‑propagation algorithm together with common activation functions.

AIBackpropagationPerceptron
0 likes · 19 min read
From Biological Neurons to Artificial Neural Networks: Perceptrons, Multilayer Perceptrons, and Backpropagation
Amap Tech
Amap Tech
Oct 16, 2020 · Artificial Intelligence

Trajectory Classification for Road Closure Detection Using Bayesian and Deep Learning Approaches

The paper proposes classifying vehicle, bicycle, and pedestrian trajectories to detect road closures, introducing a probability‑mixture Bayesian model that mitigates label noise and class imbalance through joint feature densities, and a dual‑stream deep‑learning approach encoding trajectories as images, with experiments showing the Bayesian method outperforming the neural network on limited labeled data.

Bayesian modelGPS datadeep learning
0 likes · 13 min read
Trajectory Classification for Road Closure Detection Using Bayesian and Deep Learning Approaches
Didi Tech
Didi Tech
Oct 16, 2020 · Artificial Intelligence

Mask Detection System and Visual AI Competition Achievements

Didi’s COVID‑19 mask‑detection system, built on a DFS‑based face detector and an attention‑enhanced ResNet‑50 mask classifier achieving over 99.5 % accuracy, has been deployed in vehicles, open‑sourced, and complemented by top‑ranked results in international visual AI contests, including first place in driver‑gaze prediction and podium finishes in emotion recognition and model‑compression challenges.

AIFace Detectioncomputer vision
0 likes · 22 min read
Mask Detection System and Visual AI Competition Achievements
Suning Technology
Suning Technology
Oct 15, 2020 · Artificial Intelligence

How AI Powers Offline Product Recognition in Smart Retail Stores

This lecture details the evolution of product recognition algorithms from traditional image classification to deep‑learning‑based object detection, discusses challenges in dense retail scenes, presents solutions like rotated bounding boxes and multi‑source sensor fusion, and explains practical deployment in digital and unmanned stores.

Object Detectiondeep learningdense scene
0 likes · 18 min read
How AI Powers Offline Product Recognition in Smart Retail Stores
Kuaishou Large Model
Kuaishou Large Model
Oct 15, 2020 · Artificial Intelligence

How Kuaishou’s Y‑Tech Advances Monocular Depth Estimation for Mobile AR

This article reviews Kuashou Y‑Tech’s ECCV‑2020 paper on monocular depth estimation, detailing its novel GCB‑SAB network, new HC‑Depth dataset, specialized loss functions and edge‑aware training, and demonstrates superior performance on NYUv2, TUM and real‑world mobile AR applications.

Attention MechanismMobile ARcomputer vision
0 likes · 14 min read
How Kuaishou’s Y‑Tech Advances Monocular Depth Estimation for Mobile AR
DataFunTalk
DataFunTalk
Oct 4, 2020 · Artificial Intelligence

Reinforcement Learning for Product Ranking: Model Design, Experiments, and Online Deployment

This article presents a comprehensive study of using reinforcement learning to improve e‑commerce product ranking, covering the limitations of traditional scoring, the design of context‑aware models, a pointer‑network based sequence generator, various RL algorithms, extensive offline evaluations, and successful online deployment with future research directions.

PPOdeep learninge-commerce
0 likes · 28 min read
Reinforcement Learning for Product Ranking: Model Design, Experiments, and Online Deployment
DataFunTalk
DataFunTalk
Sep 29, 2020 · Artificial Intelligence

Deep Sparse Network (NON): A Novel Deep Neural Network Model for Recommendation Systems

This article introduces the Deep Sparse Network (NON), a new deep neural architecture for recommendation systems that combines field‑wise networks, across‑field interaction networks, and an operation‑fusion network, and demonstrates its superior performance through extensive experiments and ablation studies.

CTR predictiondeep learningfeature interaction
0 likes · 14 min read
Deep Sparse Network (NON): A Novel Deep Neural Network Model for Recommendation Systems
Didi Tech
Didi Tech
Sep 29, 2020 · Artificial Intelligence

Technical Overview of Didi’s MJO 3D Panoramic Navigation, Main/Sub‑Road Yaw Detection, and Deep‑Learning‑Based Navigation Engine

Didi’s Navigation system combines a novel MJO 3D panoramic map with advanced data‑compression and octree rendering, precise main/sub‑road yaw detection using LSTM‑based models trained on GPS and image data, and a lightweight deep‑learning engine optimized for mobile CPUs/GPUs, delivering accurate, real‑time guidance for ride‑hailing and autonomous driving.

3D renderingGPS trajectorydeep learning
0 likes · 21 min read
Technical Overview of Didi’s MJO 3D Panoramic Navigation, Main/Sub‑Road Yaw Detection, and Deep‑Learning‑Based Navigation Engine
Tencent Cloud Developer
Tencent Cloud Developer
Sep 23, 2020 · Artificial Intelligence

NLP Model Interpretability: White-box and Black-box Methods and Business Applications

The article reviews NLP interpretability techniques, contrasting white‑box approaches that probe model internals such as neuron analysis, diagnostic classifiers, and attention with black‑box strategies like rationales, adversarial testing, and local surrogates, and argues that black‑box methods are generally more practical for business deployment despite offering shallower insights.

Attention MechanismBERTLIME
0 likes · 12 min read
NLP Model Interpretability: White-box and Black-box Methods and Business Applications
DataFunTalk
DataFunTalk
Sep 4, 2020 · Artificial Intelligence

Beam Search Aware Training for Optimal Tree-Based Retrieval Models

This article presents a comprehensive study of tree-based deep models for large-scale matching, introduces the theoretical framework of optimal tree models, proposes a Beam Search aware training algorithm (BSAT/OTM) to address training-test mismatch, and demonstrates significant recall improvements on Amazon Books and UserBehavior datasets.

Beam Searchdeep learninglarge-scale matching
0 likes · 23 min read
Beam Search Aware Training for Optimal Tree-Based Retrieval Models
Beike Product & Technology
Beike Product & Technology
Sep 4, 2020 · Artificial Intelligence

Wide & Deep Model for Real‑Estate Purchase Intent Prediction

This article presents a comprehensive study of the Wide & Deep architecture applied to user purchase‑intent quantification in the real‑estate domain, detailing feature engineering, model design, training procedures, experimental results, and extensions with GRU‑based sequential modeling to improve accuracy.

CTR predictionWide&Deepdeep learning
0 likes · 15 min read
Wide & Deep Model for Real‑Estate Purchase Intent Prediction
Architects' Tech Alliance
Architects' Tech Alliance
Sep 3, 2020 · Artificial Intelligence

Deep Learning Specialization Infographic Overview

This article presents a comprehensive English summary of the deep learning specialization infographics originally shared by Andrew Ng, covering fundamentals, logistic regression, shallow and deep neural networks, regularization, optimization, hyperparameters, convolutional and recurrent networks, and practical advice for model building and evaluation.

CNNOptimizationRNN
0 likes · 21 min read
Deep Learning Specialization Infographic Overview
DataFunTalk
DataFunTalk
Sep 3, 2020 · Artificial Intelligence

Deep Learning Practices for Click‑Through‑Rate Prediction and Ranking at 58.com

This article describes how 58.com applied deep‑learning techniques—including feature engineering, sample construction, model evolution from Wide&Deep to DIN/DIEN and multi‑task learning—and system‑level optimizations to improve CTR/CPM performance in its large‑scale commercial ranking platform.

CTR predictionOnline AdvertisingSystem Optimization
0 likes · 38 min read
Deep Learning Practices for Click‑Through‑Rate Prediction and Ranking at 58.com
DataFunTalk
DataFunTalk
Sep 2, 2020 · Artificial Intelligence

CSCNN: Category‑Specific Convolutional Neural Network for Visual CTR Prediction in JD E‑commerce Advertising

This article presents CSCNN, a category‑specific convolutional neural network that integrates visual priors into click‑through‑rate (CTR) models for JD.com’s e‑commerce advertising, detailing its motivation, architecture, engineering optimizations, offline and online training strategies, and empirical performance gains on both public and industrial datasets.

CTR predictioncategory-specific CNNdeep learning
0 likes · 19 min read
CSCNN: Category‑Specific Convolutional Neural Network for Visual CTR Prediction in JD E‑commerce Advertising
Taobao Frontend Technology
Taobao Frontend Technology
Sep 1, 2020 · Artificial Intelligence

Build a Browser‑Based MNIST Classifier with TensorFlow.js: A Step‑by‑Step Guide

Learn how to create a browser‑compatible MNIST image classification model using TensorFlow.js, covering data preprocessing with sprite images, model construction, training, and evaluation, while providing complete JavaScript code examples and practical tips for handling ArrayBuffer, DataView, and visualization.

JavaScriptMNISTTensorFlow.js
0 likes · 8 min read
Build a Browser‑Based MNIST Classifier with TensorFlow.js: A Step‑by‑Step Guide
58 Tech
58 Tech
Aug 31, 2020 · Artificial Intelligence

Deep Learning Practices for Commercial CTR Prediction at 58.com

This article details the end‑to‑end deep‑learning workflow for click‑through‑rate (CTR) prediction in 58.com’s commercial ranking system, covering system architecture, feature engineering, sample construction, model evolution from Wide&Deep to DIN/DIEN, and engineering optimizations that together yielded significant CPM and CVR improvements.

AdvertisingCTR predictionSystem Optimization
0 likes · 38 min read
Deep Learning Practices for Commercial CTR Prediction at 58.com
DataFunTalk
DataFunTalk
Aug 29, 2020 · Artificial Intelligence

User Modeling for Search Ranking: Practices, Model Design, and Experimental Analysis at Alibaba

This article presents Alibaba's comprehensive approach to user modeling for search CTR/CVR ranking, detailing the abstraction of user information, multi‑scale behavior processing, enhanced transformer‑based model structures, client‑side click and exposure modeling, and experimental results showing significant AUC improvements.

AlibabaAttention MechanismCTR prediction
0 likes · 18 min read
User Modeling for Search Ranking: Practices, Model Design, and Experimental Analysis at Alibaba
Suning Technology
Suning Technology
Aug 29, 2020 · Artificial Intelligence

How AI Powers Large‑Scale Time Series Forecasting and Root‑Cause Analysis

This article describes Suning's AI‑driven end‑to‑end solution for massive time‑series monitoring, anomaly detection, forecasting with DeepAR, MQ‑RNN, MQ‑CNN, ensemble methods, root‑cause localization using Hotspot and Monte‑Carlo Tree Search, and the evolution of its large‑scale log analytics platform.

Log AnalyticsRoot Cause AnalysisTime Series Forecasting
0 likes · 17 min read
How AI Powers Large‑Scale Time Series Forecasting and Root‑Cause Analysis
DataFunTalk
DataFunTalk
Aug 27, 2020 · Artificial Intelligence

Computational Advertising vs Recommendation Systems: Key Differences and Popular Models

This article explains the fundamental differences between computational advertising and recommendation systems, outlines the distinct problems each field addresses, and surveys the most widely used advertising models—including traditional machine‑learning approaches, deep‑learning architectures, and hybrid solutions—providing practical insights for engineers in both domains.

AICTR modelscomputational advertising
0 likes · 11 min read
Computational Advertising vs Recommendation Systems: Key Differences and Popular Models
Sohu Tech Products
Sohu Tech Products
Aug 19, 2020 · Artificial Intelligence

ASR Error Correction with BERT, ELECTRA and a Fuzzy‑Phoneme Generator: Techniques from Xiaomi AI

This article describes how Xiaomi's AI team tackles Automatic Speech Recognition (ASR) query errors by analyzing error patterns, employing BERT, ELECTRA and a soft‑masked BERT model, generating synthetic noisy data with a fuzzy‑phoneme generator, and presenting experimental results and future research directions.

ASRBERTELECTRA
0 likes · 18 min read
ASR Error Correction with BERT, ELECTRA and a Fuzzy‑Phoneme Generator: Techniques from Xiaomi AI
58 Tech
58 Tech
Aug 19, 2020 · Artificial Intelligence

Speech Recognition in 58.com: Application Scenarios, Data Collection, Kaldi Chain Model Practice, and End‑to‑End Exploration

This article presents a comprehensive overview of how 58.com leverages large‑scale voice data from call‑center, private phone, and micro‑chat platforms, detailing data collection, annotation, Kaldi‑based chain model training, lattice‑free techniques, and end‑to‑end Transformer‑CTC models to improve Chinese speech recognition performance.

ASRChineseEnd-to-End
0 likes · 16 min read
Speech Recognition in 58.com: Application Scenarios, Data Collection, Kaldi Chain Model Practice, and End‑to‑End Exploration
Ctrip Technology
Ctrip Technology
Aug 13, 2020 · Artificial Intelligence

Hotel Recommendation System Architecture, Models, and Evaluation at Ctrip

This article presents a comprehensive overview of Ctrip's hotel recommendation system, covering its technical architecture, data processing pipelines, various ranking and embedding models—including FM, Wide&Deep, DeepFM, and FTRL—deployment methods such as PMML and TensorFlow Serving, offline and online evaluation results, and challenges like cold‑start and diversity.

Ctripdeep learningembedding
0 likes · 24 min read
Hotel Recommendation System Architecture, Models, and Evaluation at Ctrip
Alibaba Terminal Technology
Alibaba Terminal Technology
Aug 12, 2020 · Artificial Intelligence

How AI is Revolutionizing Automatic Logic Code Generation: Techniques, Tools, and Challenges

This article surveys the landscape of automatic program synthesis for logic code, covering visual programming, example‑driven generation, code‑completion models, intent inference, NL2SQL, NL2IFTTT, and advanced frameworks like TranX and Debuild, while highlighting current challenges and research directions.

code-generationdeep learninglogic code
0 likes · 18 min read
How AI is Revolutionizing Automatic Logic Code Generation: Techniques, Tools, and Challenges
iQIYI Technical Product Team
iQIYI Technical Product Team
Aug 7, 2020 · Artificial Intelligence

Boundary Content Graph Neural Network (BC‑GNN) for Temporal Action Proposal Generation

The Boundary Content Graph Neural Network (BC‑GNN) introduces a bipartite‑graph framework that jointly refines start/end boundary probabilities and segment‑content confidence, enabling more precise temporal action proposals and achieving state‑of‑the‑art results on ActivityNet‑1.3 and THUMOS14.

BC-GNNVideo Understandingcomputer vision
0 likes · 10 min read
Boundary Content Graph Neural Network (BC‑GNN) for Temporal Action Proposal Generation
DataFunTalk
DataFunTalk
Aug 5, 2020 · Artificial Intelligence

EdgeRec: An Edge‑Computing Based Real‑Time Recommendation System

The article introduces EdgeRec, an edge‑computing powered recommendation framework that moves user‑interest perception and ranking to the client side to overcome latency in traditional cloud‑centric recommender systems, detailing its architecture, heterogeneous behavior modeling, attention‑based reranking, and experimental gains.

deep learningedge computingranking
0 likes · 13 min read
EdgeRec: An Edge‑Computing Based Real‑Time Recommendation System
58 Tech
58 Tech
Aug 3, 2020 · Artificial Intelligence

Intelligent Voice Quality Inspection System Architecture and Implementation at 58.com

The article details the design and deployment of an AI-powered intelligent voice quality inspection system at 58.com, covering its overall architecture, speech recognition, role identification, tag detection, rechecking platform, and backend infrastructure, and demonstrates its impact on call‑center efficiency and service quality.

AINatural Language Processingbackend architecture
0 likes · 12 min read
Intelligent Voice Quality Inspection System Architecture and Implementation at 58.com
Java Captain
Java Captain
Aug 2, 2020 · Artificial Intelligence

Java Spring Boot License Plate Recognition and Training System (Open‑Source)

This article introduces an open‑source Java Spring Boot project that implements a license‑plate detection and recognition system with training capabilities, detailing its features, architecture, supported plate types, software requirements, processing steps, installation guide, and reference resources.

OpenCVSpring Bootdeep learning
0 likes · 8 min read
Java Spring Boot License Plate Recognition and Training System (Open‑Source)
Tencent Advertising Technology
Tencent Advertising Technology
Jul 30, 2020 · Artificial Intelligence

Winning Strategies for the Tencent Advertising Algorithm Competition: Text Classification with Word2Vec and BiLSTM

The article details the Tencent Advertising Algorithm competition final, explains the chizhu team's approach of converting ad IDs into word sequences for text classification using large‑scale word2vec embeddings and a dual BiLSTM architecture, presents custom loss functions, training tricks, and shares full Python model code, achieving an overall rank of 11.

AdvertisingBiLSTMCompetition
0 likes · 9 min read
Winning Strategies for the Tencent Advertising Algorithm Competition: Text Classification with Word2Vec and BiLSTM
Amap Tech
Amap Tech
Jul 30, 2020 · Artificial Intelligence

Evolution and Practice of Scene Text Recognition Technology in Amap Map Data Production

Amap uses advanced scene text recognition combining detection and recognition modules, deep learning, data synthesis, and result fusion to automate map data production, achieving state-of-the-art performance and automating the majority of POI and road updates, significantly reducing labor costs.

OCRcomputer visiondeep learning
0 likes · 18 min read
Evolution and Practice of Scene Text Recognition Technology in Amap Map Data Production
Alibaba Cloud Developer
Alibaba Cloud Developer
Jul 30, 2020 · Artificial Intelligence

How Amap’s Scene Text Recognition Powers Accurate Maps: Evolution and Future Challenges

This article explains how Amap leverages scene text recognition to automate map data production, detailing the evolution from traditional image algorithms to deep‑learning models, the current detection and recognition framework, performance results, and future research directions for handling blur, data scarcity, and semantic understanding.

AmapOCRcomputer vision
0 likes · 19 min read
How Amap’s Scene Text Recognition Powers Accurate Maps: Evolution and Future Challenges
Alibaba Cloud Developer
Alibaba Cloud Developer
Jul 29, 2020 · Artificial Intelligence

How Gaode Maps Boosts Accuracy with Advanced Scene Text Recognition

This article explains how Gaode Maps leverages traditional and deep‑learning based scene text recognition techniques—including character detection, sequence models, data synthesis, and multi‑stage frameworks—to automate POI and road data production with high precision and speed.

OCRcomputer visiondeep learning
0 likes · 20 min read
How Gaode Maps Boosts Accuracy with Advanced Scene Text Recognition
Huawei Cloud Developer Alliance
Huawei Cloud Developer Alliance
Jul 29, 2020 · Artificial Intelligence

Boosting Small Industrial Image Datasets with ModelArts Augmentation and Evaluation

This article describes a practical workflow for expanding a limited industrial solar‑panel defect dataset using flip augmentation, ModelArts smart labeling, and targeted data‑balancing techniques, then evaluates the impact on a ResNet‑50 classifier with detailed accuracy and recall metrics, demonstrating how thoughtful augmentation can improve defect detection performance.

Data AugmentationModelArtsdeep learning
0 likes · 10 min read
Boosting Small Industrial Image Datasets with ModelArts Augmentation and Evaluation
Didi Tech
Didi Tech
Jul 24, 2020 · Artificial Intelligence

DLFlow: An End-to-End Deep Learning Solution for Big Data Offline Tasks

DLFlow, an end‑to‑end framework from Didi’s user‑profile team, merges Spark and TensorFlow to automate feature preprocessing, large‑scale distributed training, and massive prediction for big‑data offline tasks, offering configuration‑driven pipelines, task scheduling, and easy deployment that dramatically speeds model development.

Distributed ComputingSparkTensorFlow
0 likes · 9 min read
DLFlow: An End-to-End Deep Learning Solution for Big Data Offline Tasks
ITPUB
ITPUB
Jul 23, 2020 · Artificial Intelligence

How Likee Scales Short‑Video Recommendations with Flink, Auto‑Stats, and Cache Tensor

This article details Likee's short‑video recommendation pipeline, covering the evolution of its feature‑engineering framework, the use of Flink for minute‑level statistical and second‑level session features, the integration of automatic statistical features into DNN models, multimodal feature extraction, and the cache‑tensor technique that dramatically improves online inference performance.

AIFlinkcache tensor
0 likes · 18 min read
How Likee Scales Short‑Video Recommendations with Flink, Auto‑Stats, and Cache Tensor
Ctrip Technology
Ctrip Technology
Jul 23, 2020 · Artificial Intelligence

Inference Performance Optimization for AI Applications: Methods, Case Studies, and Future Directions

This article examines the challenges of deep learning inference, outlines general optimization methodologies—including system-level and model-level techniques—presents practical case studies such as Transformer translation model improvements, and discusses future trends in automated compilation and performance tuning for AI services.

AI inferencePerformance OptimizationTVM
0 likes · 15 min read
Inference Performance Optimization for AI Applications: Methods, Case Studies, and Future Directions
DataFunTalk
DataFunTalk
Jul 21, 2020 · Artificial Intelligence

WeChat "Look" Recommendation System: Architecture, Modeling, and Engineering Challenges

This article details the end‑to‑end technical architecture of WeChat's "Look" personalized recommendation service, covering data collection, recall, multi‑stage ranking, various CTR and multi‑objective models, reinforcement‑learning based mixing, diversity optimization, and the engineering hurdles overcome to deploy these solutions at massive scale.

CTR predictionWeChat AIdeep learning
0 likes · 17 min read
WeChat "Look" Recommendation System: Architecture, Modeling, and Engineering Challenges
DataFunTalk
DataFunTalk
Jul 20, 2020 · Artificial Intelligence

Embedding Techniques in Tencent Mobile News Recommendation System

This article reviews the practical use of embedding technologies in Tencent's mobile news recommendation pipeline, covering the fundamentals of embeddings, their historical development, item and image embeddings, user embeddings, various vector‑based recall methods, clustering strategies, and recent advances and challenges.

ClusteringTencentdeep learning
0 likes · 15 min read
Embedding Techniques in Tencent Mobile News Recommendation System
DataFunTalk
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.

Graph Neural NetworkUser ModelingWeChat AI
0 likes · 32 min read
WeChat "Look" Content Recall Architecture and Deep Learning Techniques
Jike Tech Team
Jike Tech Team
Jul 15, 2020 · Artificial Intelligence

How Embedding-Based Recall Boosted Interaction by 33% in a Live Feed

This article details how Jike's recommendation team upgraded from Spark to TensorFlow, introduced a twin‑tower embedding model for recall, deployed it with TensorFlow Serving and Elasticsearch, and achieved a 33.75% lift in user interaction on the dynamic square.

ElasticsearchTensorFlow Servingdeep learning
0 likes · 9 min read
How Embedding-Based Recall Boosted Interaction by 33% in a Live Feed
Architects Research Society
Architects Research Society
Jul 10, 2020 · Artificial Intelligence

Core Concepts and Relationships in Data Science: Big Data, Machine Learning, Data Mining, Deep Learning, and AI

This article examines six core data‑science concepts—Big Data, Machine Learning, Data Mining, Deep Learning, Artificial Intelligence, and Data Science itself—explaining their definitions, interrelationships, and how they fit together as pieces of a larger analytical puzzle.

Artificial IntelligenceData Sciencedata mining
0 likes · 17 min read
Core Concepts and Relationships in Data Science: Big Data, Machine Learning, Data Mining, Deep Learning, and AI
Youku Technology
Youku Technology
Jul 10, 2020 · Artificial Intelligence

Mastering Video Object Segmentation: Cutting-Edge Models and Design Tricks

This technical talk introduces video object segmentation tasks, reviews leading datasets and state-of-the-art deep learning models, and shares practical network design rules and performance‑boosting techniques, presented by Prof. Wang Xinggang as part of Alibaba's MEDIA AI challenge series.

AIcomputer visiondeep learning
0 likes · 4 min read
Mastering Video Object Segmentation: Cutting-Edge Models and Design Tricks
Sohu Tech Products
Sohu Tech Products
Jul 8, 2020 · Artificial Intelligence

Overview of Recommendation Systems and Their Evolution in Live Streaming Platforms

This article explains the fundamentals of recommendation systems, discusses early hotness‑based approaches, describes modern architectures with recall and ranking stages, reviews collaborative‑filtering techniques, matrix factorization, deep learning models such as NCF and NeuMF, and details how these methods are applied and optimized for live‑streaming services.

AIcollaborative filteringdeep learning
0 likes · 30 min read
Overview of Recommendation Systems and Their Evolution in Live Streaming Platforms
Laravel Tech Community
Laravel Tech Community
Jul 6, 2020 · Artificial Intelligence

Paddle.js 1.0 Released: Browser‑Based Deep Learning Framework

Paddle.js 1.0, Baidu's web‑oriented deep‑learning library, enables developers to run pretrained Paddle models directly in WebGL‑compatible browsers, offering GPU‑accelerated inference, model conversion tools, multimedia preprocessing, and a collection of ready‑made demos for on‑device AI applications.

Browser InferencePaddle.jsWeb AI
0 likes · 3 min read
Paddle.js 1.0 Released: Browser‑Based Deep Learning Framework
Alibaba Cloud Developer
Alibaba Cloud Developer
Jul 3, 2020 · Artificial Intelligence

Unlocking Visual Object Tracking: Principles, Algorithms, and Evaluation

This comprehensive review explains visual object tracking in computer vision, covering its definition, core sub‑problems of candidate generation, feature extraction, and decision making, system architecture, motion, feature and observation models, algorithm classifications, evaluation metrics, datasets, and recent research trends.

computer visiondeep learningevaluation metrics
0 likes · 30 min read
Unlocking Visual Object Tracking: Principles, Algorithms, and Evaluation
DataFunTalk
DataFunTalk
Jul 1, 2020 · Artificial Intelligence

Architecture and Implementation of Autohome's Machine Learning Platform

The article presents a comprehensive overview of Autohome's one‑stop machine learning platform, detailing its background, architecture, resource scheduling, data processing, model training (including distributed deep learning), deployment, real‑world applications such as purchase‑intent and recommendation models, and future development directions.

AutoMLDistributed TrainingKubernetes
0 likes · 19 min read
Architecture and Implementation of Autohome's Machine Learning Platform
Tencent Tech
Tencent Tech
Jul 1, 2020 · Artificial Intelligence

How Context‑Based PLC Boosts VoIP Quality in Weak Networks

This article explains why VoIP calls suffer from brief interruptions caused by packet loss, compares traditional forward error correction (FEC) and packet loss concealment (PLC) techniques, introduces Tencent's deep‑learning‑enhanced cPLC, and shows how it significantly improves MOS scores, especially under burst‑loss conditions.

FECPLCTencent
0 likes · 6 min read
How Context‑Based PLC Boosts VoIP Quality in Weak Networks
Amap Tech
Amap Tech
Jun 24, 2020 · Artificial Intelligence

Hybrid Spatio-Temporal Graph Convolutional Network (H‑STGCN) for Traffic Forecasting

The Hybrid Spatio‑Temporal Graph Convolutional Network (H‑STGCN) integrates planned traffic flow from navigation data via a domain transformer and a compound adjacency matrix, enabling graph‑based spatio‑temporal modeling that consistently outperforms baselines in real‑world traffic forecasting and reduces severe ETA errors.

H‑STGCNSpatio-temporal modelingdeep learning
0 likes · 17 min read
Hybrid Spatio-Temporal Graph Convolutional Network (H‑STGCN) for Traffic Forecasting
58 Tech
58 Tech
Jun 22, 2020 · Artificial Intelligence

Deep Learning Based Automatic QA Tool – qa_match Open‑Source Project Overview

The article reviews the open‑source qa_match tool from 58.com, detailing its deep‑learning based question‑answer matching architecture, hierarchical knowledge‑base support, lightweight pre‑training model SPTM, and practical applications, while summarizing the live‑stream presentation and Q&A session.

AIDSSMSPTM
0 likes · 5 min read
Deep Learning Based Automatic QA Tool – qa_match Open‑Source Project Overview
Youku Technology
Youku Technology
Jun 19, 2020 · Artificial Intelligence

Video-based Temporal Event Detection Methods

In the fourth Alibaba Digital Media Technology Night Talk, algorithm engineer Liu Xiaolong presents an overview of video‑based temporal event detection, covering its problem background, representative prior works, and the latest research advances within the MEDIA AI Algorithm Challenge series.

AlibabaArtificial IntelligenceTemporal Event Detection
0 likes · 1 min read
Video-based Temporal Event Detection Methods
Sohu Tech Products
Sohu Tech Products
Jun 17, 2020 · Artificial Intelligence

Ensemble Learning: Concepts, Methods, and Applications in Deep Learning

This article provides a comprehensive overview of ensemble learning, explaining its principles, common classifiers, major ensemble strategies such as bagging, boosting, and stacking, and demonstrates practical deep‑learning ensemble techniques like Dropout, test‑time augmentation, and Snapshot ensembles with code examples.

BaggingStackingboosting
0 likes · 17 min read
Ensemble Learning: Concepts, Methods, and Applications in Deep Learning
DataFunTalk
DataFunTalk
Jun 17, 2020 · Artificial Intelligence

Deep Recall and Vector Retrieval in 58 Recruitment Recommendation System

This article presents a comprehensive overview of 58's recruitment recommendation system, detailing business challenges, multi‑stage recall strategies, vector‑based deep retrieval, cost‑sensitive loss design, session optimization, online incremental training, extensive offline and online evaluations, and practical lessons for future improvements.

AIVector Retrievalcost-sensitive loss
0 likes · 15 min read
Deep Recall and Vector Retrieval in 58 Recruitment Recommendation System
DataFunTalk
DataFunTalk
Jun 13, 2020 · Artificial Intelligence

Deep Learning for Expired POI Detection at Amap: Feature Engineering, RNN, Wide&Deep, and Attention‑TCN

This article details how Amap leverages deep‑learning techniques—including temporal and auxiliary feature engineering, multi‑stage RNN models, Wide&Deep architectures, and an Attention‑TCN approach—to accurately identify and handle expired points of interest, improving map freshness and user experience.

POI expirationRNNTCN
0 likes · 13 min read
Deep Learning for Expired POI Detection at Amap: Feature Engineering, RNN, Wide&Deep, and Attention‑TCN
Tencent Cloud Developer
Tencent Cloud Developer
Jun 13, 2020 · Artificial Intelligence

Tencent Cloud Face Effects: Features, AI Techniques, Architecture, and Service Practices

Tencent Cloud’s senior engineer Li Kaibin outlines the cloud‑based face‑effects platform, detailing its AI‑driven features such as face fusion, beauty, virtual makeup, segmentation and age‑gender transformation, the CNN‑based model training pipeline, a layered service architecture with elastic scaling and robust monitoring, and future expansions into video effects, international regions and low‑code integration.

AICNNFace Effects
0 likes · 32 min read
Tencent Cloud Face Effects: Features, AI Techniques, Architecture, and Service Practices
Python Programming Learning Circle
Python Programming Learning Circle
Jun 12, 2020 · Artificial Intelligence

Visualizing Convolutional Neural Networks: Methods and Practical Examples

This article explains why visualizing CNN models is crucial for understanding and debugging deep learning systems, outlines three main visualization approaches—basic architecture, activation‑based, and gradient‑based methods—and provides step‑by‑step Keras code examples, including model summary, filter visualization, occlusion mapping, saliency maps, and class activation maps.

CNNKerasdeep learning
0 likes · 13 min read
Visualizing Convolutional Neural Networks: Methods and Practical Examples
ITPUB
ITPUB
Jun 12, 2020 · Artificial Intelligence

What’s New in qa_match V1.1? Lightweight Pre‑trained Model and One‑Level KB Support

The article introduces qa_match V1.1, an open‑source deep‑learning QA matching tool that adds one‑level knowledge‑base support, releases a lightweight Bi‑LSTM pre‑trained language model (SPTM), and provides detailed architecture, training data, performance benchmarks, future plans, and contribution guidelines.

AIdeep learningknowledge base
0 likes · 9 min read
What’s New in qa_match V1.1? Lightweight Pre‑trained Model and One‑Level KB Support
Tencent Advertising Technology
Tencent Advertising Technology
Jun 10, 2020 · Artificial Intelligence

Improving Advertising Inventory Forecasting with Deep Spatial‑Temporal Tensor Factorization

The article explains how advertising inventory forecasting—predicting how many users will see a specific ad—poses challenges due to fluctuating traffic and user segmentation, and describes a new deep spatial‑temporal tensor factorization model that dramatically improves prediction accuracy, scalability, and robustness for large‑scale ad platforms.

AIAdvertisingTime Series
0 likes · 11 min read
Improving Advertising Inventory Forecasting with Deep Spatial‑Temporal Tensor Factorization
Alibaba Cloud Developer
Alibaba Cloud Developer
Jun 9, 2020 · Artificial Intelligence

How AI Predicts Food Delivery Times: Insights from Alibaba’s KDD 2020 Study

Alibaba’s local life smart logistics team presents a KDD 2020 oral paper detailing a deep‑learning model for order fulfillment cycle time (OFCT) estimation in on‑demand food delivery, describing feature engineering, rider encoding, post‑processing operators, and experimental results that significantly improve prediction accuracy and user experience.

Food DeliveryKDD2020deep learning
0 likes · 15 min read
How AI Predicts Food Delivery Times: Insights from Alibaba’s KDD 2020 Study
58 Tech
58 Tech
Jun 5, 2020 · Artificial Intelligence

qa_match V1.1: Upgraded Lightweight Deep Learning QA Matching Tool

The article introduces qa_match V1.1, an open‑source, Apache‑licensed lightweight question‑answer matching system that adds a simple pre‑trained language model (SPTM), supports one‑level knowledge bases, details model architecture, training resources, performance benchmarks, future plans, and contribution guidelines.

AIdeep learningknowledge base
0 likes · 10 min read
qa_match V1.1: Upgraded Lightweight Deep Learning QA Matching Tool
Ctrip Technology
Ctrip Technology
Jun 4, 2020 · Artificial Intelligence

Semantic Matching Models for Travel QA: Deep Learning Techniques, Interaction Models, and Transfer Learning

This article reviews the evolution of semantic matching models for travel question‑answering, covering traditional keyword and probabilistic methods, deep‑learning encoders such as LSTM, CNN, and Transformer, interaction‑based architectures like MatchPyramid and hCNN, as well as transfer‑learning and multilingual extensions to improve practical deployment.

Natural Language Processingcontext modelingdeep learning
0 likes · 21 min read
Semantic Matching Models for Travel QA: Deep Learning Techniques, Interaction Models, and Transfer Learning
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.

Vector Retrievaldeep learninge-commerce
0 likes · 11 min read
Semantic Retrieval and Product Ranking in JD E‑commerce Search
58 Tech
58 Tech
Jun 3, 2020 · Artificial Intelligence

Speaker Verification System for Detecting Spam Calls in 58 Used‑Car Platform

This article describes how the 58 used‑car team built a speaker‑verification pipeline—covering data collection, MFCC feature extraction, LSTM and GMM modeling, threshold tuning, multi‑speaker clustering, and deployment results—to automatically block nuisance telemarketing calls while preserving user privacy.

GMMLSTMMFCC
0 likes · 15 min read
Speaker Verification System for Detecting Spam Calls in 58 Used‑Car Platform
Alibaba Cloud Developer
Alibaba Cloud Developer
Jun 2, 2020 · Artificial Intelligence

How FashionBERT Boosts E‑Commerce Image‑Text Matching with Patch Embeddings

This article introduces FashionBERT, a multimodal BERT‑based model that replaces ROI‑based image tokens with uniform image patches to overcome e‑commerce specific challenges, details its architecture, adaptive loss balancing, deployment in Alibaba search, and reports significant performance gains on public and internal datasets.

BERTImage-Text MatchingMultimodal
0 likes · 13 min read
How FashionBERT Boosts E‑Commerce Image‑Text Matching with Patch Embeddings
DataFunTalk
DataFunTalk
May 29, 2020 · Artificial Intelligence

Model‑Independent Learning: Multi‑Task Learning and Transfer Learning

This article explains two model‑independent learning paradigms—multi‑task learning and transfer learning—detailing their motivations, sharing mechanisms, training procedures, theoretical formulations, and practical benefits such as improved generalization, data efficiency, and domain‑invariant representations.

Domain Adaptationdeep learningmachine learning
0 likes · 21 min read
Model‑Independent Learning: Multi‑Task Learning and Transfer Learning
JD Retail Technology
JD Retail Technology
May 27, 2020 · Artificial Intelligence

JD ARVR Tech Department Publishes Two Papers on Defocus Blur Detection and Few-Shot Learning in Top Venues

The JD ARVR technology department announced two peer‑reviewed papers—one on a novel defocus blur detection network published in Transaction on Multimedia and another on a transductive relation‑propagation network for few‑shot learning accepted at IJCAI 2020—highlighting their advanced AI research and future AR‑VR ecosystem plans.

ARVRFew-Shot Learningcomputer vision
0 likes · 7 min read
JD ARVR Tech Department Publishes Two Papers on Defocus Blur Detection and Few-Shot Learning in Top Venues
Didi Tech
Didi Tech
May 25, 2020 · Artificial Intelligence

How Didi Harnesses Cutting‑Edge Speech Recognition: From ASR Basics to Transformer Models

This article provides a comprehensive technical overview of modern speech recognition, covering Didi’s driver‑assistant and smart‑customer‑service applications, fundamental ASR concepts, classic GMM‑HMM methods, deep‑learning breakthroughs such as DNN‑HMM, CTC, attention‑based and transformer models, practical training tricks, signal‑processing steps, and multimodal fusion techniques.

ASRAttentionCTC
0 likes · 16 min read
How Didi Harnesses Cutting‑Edge Speech Recognition: From ASR Basics to Transformer Models
Amap Tech
Amap Tech
May 25, 2020 · Artificial Intelligence

Automated Production Line for Base Map Data Using Image AI and Data Fusion

Gaode’s automated production line combines deep‑learning image recognition, GPS‑enhanced location services, image differencing with semantic filtering, and standardized data‑fusion to continuously refresh China’s national base map, cutting manual effort and costs while delivering real‑time, high‑quality map updates for road traffic infrastructure.

computer visiondata fusiondeep learning
0 likes · 11 min read
Automated Production Line for Base Map Data Using Image AI and Data Fusion
DataFunTalk
DataFunTalk
May 23, 2020 · Artificial Intelligence

iQIYI Deep Semantic Representation Learning Framework for Video Recommendation and Search

Based on academic and industry experience, iQIYI has designed a deep semantic representation learning framework that integrates multimodal side information and deep models such as Transformers and graph neural networks, improving recall, ranking, deduplication, diversity and semantic matching across recommendation and search scenarios.

Graph Neural NetworksMultimodaldeep learning
0 likes · 27 min read
iQIYI Deep Semantic Representation Learning Framework for Video Recommendation and Search
Meituan Technology Team
Meituan Technology Team
May 21, 2020 · Artificial Intelligence

CenterMask: Single-Shot Instance Segmentation with Point Representation

CenterMask is a single‑shot, anchor‑free instance segmentation framework that predicts a coarse shape from each object’s center point and a full‑image saliency map, multiplies them to produce precise masks, and achieves competitive COCO AP while running faster than two‑stage methods like Mask R-CNN.

CenterMaskObject Detectiondeep learning
0 likes · 15 min read
CenterMask: Single-Shot Instance Segmentation with Point Representation
Alibaba Cloud Developer
Alibaba Cloud Developer
May 21, 2020 · Artificial Intelligence

How DeepMatch Boosts Music Recommendations with Play Rate and Intent Signals

This article examines the DeepMatch retrieval model for Tmall Genie music recommendation, detailing how incorporating user feedback such as play‑rate and query intent signals via multi‑task learning and feedback‑aware self‑attention improves recall accuracy and reduces negative recommendations, while also discussing embedding factorization, loss functions, and distributed training optimizations.

deep learningmultitask learningrecommendation systems
0 likes · 18 min read
How DeepMatch Boosts Music Recommendations with Play Rate and Intent Signals
DataFunTalk
DataFunTalk
May 15, 2020 · Artificial Intelligence

Optimizing Sparse Feature Embedding for Large‑Scale Recommendation and CTR Prediction

The article reviews recent research on representing massive sparse features in click‑through‑rate (CTR) models, introducing Alibaba's Res‑embedding method and Google's Neural Input Search (NIS) approach, and discusses how these techniques improve embedding efficiency and model generalization in large‑scale recommendation systems.

CTR predictiondeep learningfeature embedding
0 likes · 10 min read
Optimizing Sparse Feature Embedding for Large‑Scale Recommendation and CTR Prediction
Python Programming Learning Circle
Python Programming Learning Circle
May 12, 2020 · Artificial Intelligence

Batch Image Segmentation with Python and PaddlePaddle

This tutorial demonstrates how to use Python and the PaddlePaddle deep‑learning platform to automatically remove backgrounds from multiple photos in one step, covering installation, verification, and a concise five‑line code example for batch human segmentation.

Batch ProcessingPaddlePaddlecomputer vision
0 likes · 6 min read
Batch Image Segmentation with Python and PaddlePaddle
DataFunTalk
DataFunTalk
May 11, 2020 · Artificial Intelligence

Advances in Click‑Through Rate Prediction: Deep Spatio‑Temporal Networks, Memory Networks, and Feature Expression Learning

This article reviews recent innovations in CTR prediction for an intelligent marketing platform, covering deep spatio‑temporal networks, deep memory networks, and a feature‑expression‑assisted learning framework, with system architecture details, experimental results, and references to KDD and IJCAI papers.

AdvertisingCTR predictiondeep learning
0 likes · 15 min read
Advances in Click‑Through Rate Prediction: Deep Spatio‑Temporal Networks, Memory Networks, and Feature Expression Learning
21CTO
21CTO
May 10, 2020 · Artificial Intelligence

How AI Restored a Century‑Old Beijing Film in Vibrant Color

An AI‑driven pipeline—using DAIN for frame interpolation, ESRGAN for super‑resolution, and DeOldify for colorization—transformed a low‑resolution 1920s black‑and‑white Beijing footage into a smooth, 4K, fully colored video, showcasing both technical challenges and cultural impact.

AI video restorationDAINDeOldify
0 likes · 11 min read
How AI Restored a Century‑Old Beijing Film in Vibrant Color
Amap Tech
Amap Tech
May 8, 2020 · Artificial Intelligence

Expired POI Detection in Amap Using Deep Learning: Feature Engineering, RNN, Wide&Deep, and TCN Models

The project develops a deep‑learning pipeline for Amap’s expired POI detection that integrates two‑year temporal trend features, industry and verification attributes, a variable‑length LSTM, a Wide‑Deep architecture, and a Wide‑Attention Temporal Convolutional Network, achieving higher accuracy and efficiency while outlining future macro‑and micro‑level enhancements.

POI expirationRNNTCN
0 likes · 15 min read
Expired POI Detection in Amap Using Deep Learning: Feature Engineering, RNN, Wide&Deep, and TCN Models
Programmer DD
Programmer DD
Apr 24, 2020 · Artificial Intelligence

Turn Photos into Studio Ghibli‑Style Anime with AnimeGAN – A Hands‑On Guide

This article introduces AnimeGAN, a lightweight GAN that converts real photos into Japanese anime‑style illustrations, explains its architecture, loss functions, model size advantages, and provides step‑by‑step instructions with code for setting up, training, and testing the TensorFlow implementation.

AnimeGANGaNTensorFlow
0 likes · 8 min read
Turn Photos into Studio Ghibli‑Style Anime with AnimeGAN – A Hands‑On Guide
DataFunTalk
DataFunTalk
Apr 21, 2020 · Artificial Intelligence

Attention Mechanisms in Deep Learning Recommendation Models: A Survey

This article surveys the application of attention mechanisms in deep learning recommendation systems, reviewing models such as AFM, DIN, DIEN, DSIN, Behavior Sequence Transformer, Deep Spatio‑Temporal Networks, and ATRank, and discusses their architectures, attention types, advantages, and limitations.

AttentionCTR predictiondeep learning
0 likes · 10 min read
Attention Mechanisms in Deep Learning Recommendation Models: A Survey
DataFunTalk
DataFunTalk
Apr 20, 2020 · Artificial Intelligence

Video Search at Youku: Algorithmic Practices, Relevance, Ranking, and Multimodal Techniques

This article presents a comprehensive overview of Youku's video search system, covering business background, evaluation metrics, system and algorithm frameworks, relevance and ranking feature engineering, dataset construction, semantic matching, multimodal video understanding, and practical case studies that illustrate the impact of deep learning and AI techniques on search performance.

AIMultimodaldeep learning
0 likes · 18 min read
Video Search at Youku: Algorithmic Practices, Relevance, Ranking, and Multimodal Techniques
DataFunTalk
DataFunTalk
Apr 13, 2020 · Artificial Intelligence

Deep Spatio‑Temporal Neural Networks and Memory‑Augmented DNN for Click‑Through Rate Prediction

This article presents the design, challenges, and experimental evaluation of DSTN (with pooling, self‑attention, and interactive‑attention variants) and MA‑DNN models for CTR prediction, highlighting how temporal and contextual ad information improves accuracy and yields significant online gains in large‑scale advertising systems.

AdvertisingCTR predictionKDD
0 likes · 16 min read
Deep Spatio‑Temporal Neural Networks and Memory‑Augmented DNN for Click‑Through Rate Prediction
DataFunTalk
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.

Graph Neural Networkdeep learningknowledge graph
0 likes · 14 min read
Wang Zhe’s Machine Learning Notes – Answers to Frequently Asked Questions on Recommendation Systems
Huajiao Technology
Huajiao Technology
Apr 7, 2020 · Artificial Intelligence

How Huajiao Live Built a From‑Scratch Personalized Recommendation System

This article analyzes Huajiao Live's end‑to‑end recommendation pipeline, covering basic concepts, recall and ranking algorithms—including collaborative filtering, matrix factorization, deep learning models—and multi‑objective optimization, while detailing the engineering workflow for training, deployment, and real‑time serving in a live‑streaming environment.

AIcollaborative filteringdeep learning
0 likes · 17 min read
How Huajiao Live Built a From‑Scratch Personalized Recommendation System
DataFunTalk
DataFunTalk
Apr 6, 2020 · Artificial Intelligence

Introducing DeepMatch: An Open‑Source Library for Deep Retrieval Matching Algorithms

DeepMatch is an open‑source Python library that implements several mainstream deep‑learning based recall‑matching algorithms, provides easy installation via pip, detailed usage examples with code, and supports exporting user and item vectors for ANN search, making it ideal for rapid experimentation and learning in recommendation systems.

ANNPythonVector Retrieval
0 likes · 10 min read
Introducing DeepMatch: An Open‑Source Library for Deep Retrieval Matching Algorithms
JD Retail Technology
JD Retail Technology
Apr 2, 2020 · Artificial Intelligence

How Deep Learning Powers Text Detection in E‑commerce Posters

This article surveys state‑of‑the‑art deep‑learning techniques for scene text detection and recognition in e‑commerce poster images, detailing models such as CTPN, TextBoxes, SegLink, EAST, and end‑to‑end frameworks, and discusses their architectures, strengths, limitations, and future challenges.

computer visiondeep learninge-commerce
0 likes · 16 min read
How Deep Learning Powers Text Detection in E‑commerce Posters
58 Tech
58 Tech
Apr 1, 2020 · Artificial Intelligence

Intelligent Recommendation System for 58 Tongzhen: Architecture, Data, Features, and Model Evolution

This article describes how 58 Tongzhen leverages AI technologies—including data pipelines, feature engineering, various recall and ranking models, and AB‑testing—to build a personalized feed recommendation system for the down‑market, detailing its overall architecture, data sources, model iterations, performance gains, and future directions.

AB TestingAIRanking Models
0 likes · 20 min read
Intelligent Recommendation System for 58 Tongzhen: Architecture, Data, Features, and Model Evolution