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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 predictionDeep LearningReinforcement 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.

Deep LearningEmbeddingTencent
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.

Deep LearningGraph Neural NetworkWeChat 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.

Deep LearningElasticsearchEmbedding
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 ScienceDeep Learning
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.

AIDeep LearningRecommendation Systems
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 InferenceDeep LearningPaddle.js
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.

AutoMLDeep LearningDistributed Training
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.

Deep LearningFECPLC
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.

Deep LearningH‑STGCNSpatio-temporal modeling
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.

AIDSSMDeep Learning
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 IntelligenceComputer Vision
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.

Deep LearningStackingbagging
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.

AIDeep LearningVector Retrieval
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.

Deep LearningPOI expirationRNN
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.

AICNNCloud Services
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.

CNNDeep LearningKeras
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.

AIAdvertisingDeep Learning
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.

Deep LearningKDD2020Logistics
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.

Deep Learningcontext modelingnatural language processing
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.

Deep LearningVector Retrievale‑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.

Deep LearningGMMLSTM
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.

BERTDeep LearningMultimodal
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.

Deep Learningdomain adaptationmachine 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.

ARVRComputer VisionDeep Learning
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.

ASRCTCDeep Learning
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 VisionDeep Learningdata fusion
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.

Deep LearningMultimodalRecommendation Systems
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.

CenterMaskDeep Learningobject detection
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 LearningRecommendation SystemsSelf-Attention
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 LearningRecommendation Systems
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 ProcessingComputer VisionDeep Learning
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.

Deep LearningPOI expirationRNN
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.

AnimeGANDeep LearningGAN
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.

CTR predictionDeep LearningRecommendation Systems
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.

AIDeep LearningMultimodal
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 predictionDeep Learning
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.

Deep LearningGraph Neural NetworkModel Evaluation
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.

AIDeep Learningcollaborative filtering
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.

ANNDeep LearningPython
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 testingAIDeep Learning
0 likes · 20 min read
Intelligent Recommendation System for 58 Tongzhen: Architecture, Data, Features, and Model Evolution
58 Tech
58 Tech
Mar 30, 2020 · Artificial Intelligence

Embedding Techniques for Advertising Recall and Ranking in a Second-Hand Car Platform

This article details the commercial strategy team's exploration of embedding technologies for a second‑hand car platform, covering mainstream embedding methods, their application in advertising recall and ranking pipelines, system architecture, model optimizations, evaluation results, and future directions.

AdvertisingDSSMDeep Learning
0 likes · 22 min read
Embedding Techniques for Advertising Recall and Ranking in a Second-Hand Car Platform
58 Tech
58 Tech
Mar 27, 2020 · Artificial Intelligence

dl_inference: Open‑Source General Deep Learning Inference Service

dl_inference is an open‑source inference platform that simplifies deployment of TensorFlow and PyTorch models in production, offering unified gRPC access, load‑balanced multi‑node serving, GPU/CPU options, customizable pre‑ and post‑processing, and extensible architecture for future AI workloads.

AI inferenceDeep LearningModel Serving
0 likes · 11 min read
dl_inference: Open‑Source General Deep Learning Inference Service
DataFunTalk
DataFunTalk
Mar 26, 2020 · Artificial Intelligence

Building a Personalized Live‑Streaming Recommendation System: From Basics to Advanced Models at Huajiao Live

This article explains how Huajiao Live designed and evolved its live‑streaming recommendation system, covering basic concepts, collaborative‑filtering and matrix‑factorization techniques, deep‑learning models, ranking and multi‑objective optimization, and practical deployment considerations for real‑time personalized content delivery.

Deep LearningHuajiaocollaborative filtering
0 likes · 16 min read
Building a Personalized Live‑Streaming Recommendation System: From Basics to Advanced Models at Huajiao Live
Huajiao Technology
Huajiao Technology
Mar 24, 2020 · Artificial Intelligence

How to Overcome Recommendation Cold Start: Methods and Huajiao Live’s Real‑World Practices

This article explains the cold‑start problem in recommendation systems, outlines common industry solutions such as popular‑content, group‑representative, auxiliary‑information, bandit algorithms, and deep learning, and details how Huajiao Live applied these techniques to improve new‑user engagement and metrics.

Deep LearningHuajiao Livebandit algorithm
0 likes · 13 min read
How to Overcome Recommendation Cold Start: Methods and Huajiao Live’s Real‑World Practices
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
Mar 22, 2020 · Artificial Intelligence

Entity and Relation Extraction: QA-Style Overview of Methods, Challenges, and Recent Advances

This article provides a comprehensive QA‑style review of entity‑relation extraction (ERE), covering pipeline drawbacks, various decoding strategies for NER, common relation‑classification techniques, shared‑parameter and joint‑decoding models, recent transformer‑based approaches, challenges such as overlapping entities, low‑resource settings, and the use of graph neural networks.

Deep LearningNLPentity extraction
0 likes · 32 min read
Entity and Relation Extraction: QA-Style Overview of Methods, Challenges, and Recent Advances
DataFunTalk
DataFunTalk
Mar 18, 2020 · Artificial Intelligence

Personalized Push Notification System: Embedding, Recall, and Ranking Techniques at Meitu

This article presents a comprehensive technical overview of Meitu's personalized push notification pipeline, detailing the evolution of embedding methods (Word2Vec, Airbnb listing embedding, graph embedding), multiple recall strategies (global, personalized, attribute, and content‑based), and a progression of ranking models from logistic regression to field‑wise three‑tower architectures, highlighting their impact on click‑through rates.

AIDeep LearningPush Notification
0 likes · 12 min read
Personalized Push Notification System: Embedding, Recall, and Ranking Techniques at Meitu
DataFunTalk
DataFunTalk
Mar 16, 2020 · Artificial Intelligence

Phoenix News Feed Recommendation System: Architecture, Modeling, and Feature Engineering

This article presents a comprehensive overview of Phoenix News's AI‑driven feed recommendation system, detailing its business challenges, multi‑stage architecture, deep learning models, feature pipelines, metric trade‑offs, cold‑start solutions, and practical insights for improving user satisfaction and content quality.

AIDeep Learningfeature engineering
0 likes · 22 min read
Phoenix News Feed Recommendation System: Architecture, Modeling, and Feature Engineering
Alibaba Cloud Developer
Alibaba Cloud Developer
Mar 13, 2020 · Artificial Intelligence

How Deep Match to Rank Boosts CTR Prediction in E‑Commerce Recommendations

The article presents the Deep Match to Rank (DMR) model, which integrates collaborative‑filtering inspired user‑to‑item relevance modeling into the ranking stage of recommendation systems, achieving significant offline and online improvements in click‑through rate and revenue metrics for e‑commerce platforms.

CTR predictionDeep Learninge‑commerce
0 likes · 11 min read
How Deep Match to Rank Boosts CTR Prediction in E‑Commerce Recommendations
DataFunTalk
DataFunTalk
Mar 12, 2020 · Artificial Intelligence

Model Evolution and Optimization for Recommendation Systems in a Mid‑size E‑commerce App

This article describes the end‑to‑end recommendation pipeline of the Province Money Fast Report app, covering business background, data collection, model training and evaluation, the evolution from FM to DeepFM, DIN, DCN, xDeepFM, ESMM and custom networks, as well as serving strategies and practical lessons learned.

CTR predictionDeep LearningModel Serving
0 likes · 28 min read
Model Evolution and Optimization for Recommendation Systems in a Mid‑size E‑commerce App
21CTO
21CTO
Mar 11, 2020 · Artificial Intelligence

Understanding the AI Revolution: From Basics to Future Impact

This article explains the rapid rise of artificial intelligence, outlines China's national AI development plan, compares human and machine intelligence, discusses current AI applications, future job impacts, and the infrastructure that will power the next industrial revolution.

AI applicationsArtificial IntelligenceDeep Learning
0 likes · 10 min read
Understanding the AI Revolution: From Basics to Future Impact
58 Tech
58 Tech
Mar 11, 2020 · Artificial Intelligence

qa_match: An Open‑Source Deep Learning Based Question‑Answer Matching System

The article introduces qa_match, an open‑source lightweight QA matching tool built on TensorFlow that combines BiLSTM‑based domain classification, DSSM‑based intent matching, and a model‑fusion strategy to deliver accurate, multi‑type responses for intelligent customer service applications.

AIBiLSTMDSSM
0 likes · 12 min read
qa_match: An Open‑Source Deep Learning Based Question‑Answer Matching System
Alibaba Cloud Developer
Alibaba Cloud Developer
Mar 10, 2020 · Artificial Intelligence

Can Frequency‑Domain Learning Boost Image Inference Efficiency?

This article presents a system‑level approach that performs deep‑learning inference directly on JPEG frequency components, uses a gating mechanism to select important DCT coefficients, and demonstrates higher accuracy with far lower bandwidth for image classification and instance segmentation tasks.

Bandwidth ReductionComputer VisionDeep Learning
0 likes · 22 min read
Can Frequency‑Domain Learning Boost Image Inference Efficiency?
JD Tech Talk
JD Tech Talk
Mar 9, 2020 · Artificial Intelligence

Advances in Deep Learning for Content Recommendation and User Behavior Modeling by JD Digits

The article reviews recent deep‑learning breakthroughs in personalized content recommendation, covering news and e‑commerce systems, JD Digits' multi‑dimensional user behavior prediction models, knowledge‑graph meta‑learning, and the impact of multimodal AI on future recommendation technologies.

Deep LearningMultimodal AIRecommendation Systems
0 likes · 6 min read
Advances in Deep Learning for Content Recommendation and User Behavior Modeling by JD Digits
Qunar Tech Salon
Qunar Tech Salon
Mar 4, 2020 · Artificial Intelligence

Deep Match to Rank (DMR) Model for Personalized Click‑Through Rate Prediction

The paper proposes the Deep Match to Rank (DMR) model, which integrates matching‑stage collaborative‑filtering ideas into the ranking stage to explicitly represent user‑to‑item relevance, thereby enhancing personalization and achieving significant CTR and DPV improvements in e‑commerce recommendation scenarios.

CTR predictionDeep LearningRecommendation Systems
0 likes · 12 min read
Deep Match to Rank (DMR) Model for Personalized Click‑Through Rate Prediction
TAL Education Technology
TAL Education Technology
Feb 28, 2020 · Artificial Intelligence

TPNN Multi‑GPU Training and Mobile Optimization for Children's Acoustic Speech Recognition Models

This article describes the TPNN deep‑learning platform’s multi‑GPU acceleration, data‑parallel BMUF training, LSTM‑CTC acoustic modeling, and a suite of mobile‑side optimizations—including model pruning, 8‑bit quantization, low‑precision matrix multiplication and mixed‑precision computation—that together achieve over 92% recognition accuracy for children’s English speech on both server and mobile devices.

BMUFCTCDeep Learning
0 likes · 15 min read
TPNN Multi‑GPU Training and Mobile Optimization for Children's Acoustic Speech Recognition Models
Tencent Advertising Technology
Tencent Advertising Technology
Feb 28, 2020 · Artificial Intelligence

Bayesian Smoothing and Key-Value Memory Networks for Click-Through Rate Prediction in Recommendation Systems

This article presents a Bayesian smoothing approach to alleviate cold-start problems in click-through rate estimation, introduces key-value memory networks to incorporate prior knowledge, and proposes methods to convert continuous features into dictionary embeddings for deep learning models in recommendation systems.

Deep Learningclick-through ratecontinuous feature embedding
0 likes · 18 min read
Bayesian Smoothing and Key-Value Memory Networks for Click-Through Rate Prediction in Recommendation Systems
Tencent Tech
Tencent Tech
Feb 27, 2020 · Artificial Intelligence

How to Speed Up Deep Learning Models: Cutting-Edge Acceleration Techniques

Deep learning models often suffer from slow training and deployment due to their size, but a range of advanced acceleration methods—including model architecture optimization, pruning, quantization, knowledge distillation, and distributed training techniques—can dramatically improve speed and efficiency while maintaining performance.

Deep LearningDistributed Trainingknowledge distillation
0 likes · 14 min read
How to Speed Up Deep Learning Models: Cutting-Edge Acceleration Techniques
Alibaba Cloud Developer
Alibaba Cloud Developer
Feb 25, 2020 · Artificial Intelligence

How Attribute‑Specific Embedding Networks Revolutionize Fashion Copyright Protection

A new AI algorithm jointly developed by Alibaba Security and Zhejiang University learns fine‑grained, attribute‑aware similarity embeddings for fashion images, enabling accurate detection of local design plagiarism and improving retrieval performance across multiple benchmark datasets.

Computer VisionDeep Learningattribute embedding
0 likes · 14 min read
How Attribute‑Specific Embedding Networks Revolutionize Fashion Copyright Protection
DataFunTalk
DataFunTalk
Feb 22, 2020 · Artificial Intelligence

Double DNN Ranking Model with Online Knowledge Distillation for Real‑Time Recommendation at iQIYI

The article introduces iQIYI's double‑DNN ranking architecture that combines a high‑performance teacher network with a lightweight student network through online knowledge distillation, detailing the evolution of deep learning‑based ranking models, the motivation for model upgrades, training pipelines, and experimental results that demonstrate significant latency reduction and ROI improvement.

Deep LearningOnline LearningRanking Models
0 likes · 13 min read
Double DNN Ranking Model with Online Knowledge Distillation for Real‑Time Recommendation at iQIYI
DataFunTalk
DataFunTalk
Feb 20, 2020 · Artificial Intelligence

Perception Technology for Autonomous Heavy Trucks: Methods, Challenges, and Production Considerations

This article reviews perception technologies used in autonomous heavy‑truck systems—including lane‑line detection, obstacle detection, and LiDAR sensing—detailing traditional and deep‑learning approaches, practical challenges on high‑speed highways, and the cost, performance, and reliability issues faced when moving these solutions to mass production.

Deep LearningLiDARautonomous driving
0 likes · 16 min read
Perception Technology for Autonomous Heavy Trucks: Methods, Challenges, and Production Considerations
DataFunTalk
DataFunTalk
Feb 13, 2020 · Artificial Intelligence

Deep Learning Techniques and Challenges in Autonomous Driving

This article reviews the rapid development of deep learning, its pivotal role in autonomous driving, outlines end‑to‑end perception‑to‑control pipelines, discusses the strengths and limitations of deep models, and proposes practical strategies such as task decomposition, multi‑method fusion, and sensor integration to improve safety and interpretability.

Computer VisionDeep LearningEnd-to-End
0 likes · 8 min read
Deep Learning Techniques and Challenges in Autonomous Driving
DataFunTalk
DataFunTalk
Feb 10, 2020 · Artificial Intelligence

Real‑Time Intelligent Anomaly Detection Platform at Ctrip: Integrating Flink and TensorFlow (Prophet)

The article describes Ctrip's Prophet platform, which combines Flink real‑time stream processing with TensorFlow deep‑learning models to provide intelligent, low‑latency anomaly detection, replacing traditional rule‑based alerts and addressing challenges such as holiday traffic and model scalability.

AIDeep LearningFlink
0 likes · 13 min read
Real‑Time Intelligent Anomaly Detection Platform at Ctrip: Integrating Flink and TensorFlow (Prophet)
Python Programming Learning Circle
Python Programming Learning Circle
Feb 8, 2020 · Artificial Intelligence

Nine Recommended Programming Books for Home Learning

During the stay‑at‑home period, this article suggests nine concise programming books covering Python neural networks, web crawling, deep learning with PyTorch, machine learning fundamentals, zero‑trust network security, classic programming pearls, Python mathematics, AI algorithms, and Vim text processing, each with brief descriptions and images.

AIDeep LearningVim
0 likes · 9 min read
Nine Recommended Programming Books for Home Learning
Python Programming Learning Circle
Python Programming Learning Circle
Feb 8, 2020 · Artificial Intelligence

Neural Network Construction Example with Python Implementation

This article presents a comprehensive tutorial on building and training a multi‑layer neural network in Python, covering data preprocessing, model architecture definition, parameter initialization, forward and backward propagation, cost computation, and parameter updates with code examples for activation functions and optimization techniques.

Deep Learning
0 likes · 13 min read
Neural Network Construction Example with Python Implementation
Meituan Technology Team
Meituan Technology Team
Feb 6, 2020 · Artificial Intelligence

Building a One-Stop Machine Learning Platform: Meituan's Turing Platform

Meituan’s Turing platform consolidates the entire delivery‑order workflow—from massive data ingestion and feature generation to model training, evaluation, deployment, real‑time prediction, and AB testing—into a single, end‑to‑end system that evolved from a minimal MVP into a fully platformized solution, addressing speed, accuracy, and engineering‑algorithm decoupling while planning deeper deep‑learning integration.

AB testingDeep LearningMachine Learning Platform
0 likes · 16 min read
Building a One-Stop Machine Learning Platform: Meituan's Turing Platform
DataFunTalk
DataFunTalk
Feb 3, 2020 · Artificial Intelligence

Advances in Speech Recognition: Concepts, Deep Learning Methods, and Didi’s Applications

This article presents a comprehensive overview of modern speech recognition technology, covering basic ASR concepts, classic acoustic and language models, deep‑learning approaches such as DNN‑HMM, CTC, attention‑based and transformer models, multimodal fusion, signal‑processing pipelines, and practical deployment considerations at Didi.

ASRCTCDeep Learning
0 likes · 15 min read
Advances in Speech Recognition: Concepts, Deep Learning Methods, and Didi’s Applications
Huajiao Technology
Huajiao Technology
Jan 21, 2020 · Artificial Intelligence

Overview of Ranking Algorithms in Recommendation Systems

This article reviews the evolution of ranking models in modern recommendation systems, covering traditional linear models, factorization machines, tree‑based GBDT+LR, and a range of deep learning architectures such as Wide&Deep, DeepFM, DCN, xDeepFM, DIN, as well as multi‑task frameworks like ESMM and MMOE, and finally illustrates their practical deployment in a live streaming platform.

Deep LearningRecommendation Systemsfeature engineering
0 likes · 20 min read
Overview of Ranking Algorithms in Recommendation Systems
Xueersi Online School Tech Team
Xueersi Online School Tech Team
Jan 17, 2020 · Artificial Intelligence

Fine‑tuning BERT for Sentence Pair Similarity in an Online Education Platform

This article describes how a BERT‑based model is fine‑tuned to compute sentence‑pair similarity for improving recommendation accuracy in an online school, detailing the architecture, training mechanisms, code implementation, experimental results, and future extensions such as sentiment analysis.

BERTChinese NLPDeep Learning
0 likes · 20 min read
Fine‑tuning BERT for Sentence Pair Similarity in an Online Education Platform
DataFunTalk
DataFunTalk
Jan 16, 2020 · Artificial Intelligence

Voice Conversion: Fundamentals, Methods, and iQIYI Applications

This article provides a comprehensive overview of voice conversion technology, covering its definition, parallel and non‑parallel data approaches, classic and deep‑learning methods such as DTW, GMM, seq2seq, PPG, VAE, Flow, GAN, and practical applications and challenges in iQIYI’s products.

ASRDeep LearningGAN
0 likes · 8 min read
Voice Conversion: Fundamentals, Methods, and iQIYI Applications
Tencent Cloud Developer
Tencent Cloud Developer
Jan 14, 2020 · Artificial Intelligence

MedicalNet: Tencent's Pre-trained Model for 3D Medical Imaging AI

MedicalNet, Tencent’s open-source framework, aggregates diverse small 3D medical imaging datasets into a large pre-training corpus, applies dataset filtering and joint spatial-pixel normalization, and provides encoder-decoder models that accelerate convergence and boost accuracy for AI-driven diagnosis in data-scarce medical imaging scenarios.

3D Medical ImagingDeep LearningHealthcare AI
0 likes · 5 min read
MedicalNet: Tencent's Pre-trained Model for 3D Medical Imaging AI
Alibaba Cloud Developer
Alibaba Cloud Developer
Jan 10, 2020 · Artificial Intelligence

How AI Powers Ground Marker Recognition for High‑Precision Maps

This article details the evolution of ground‑marker recognition technology in high‑precision maps, covering challenges of diverse and worn markings, traditional segmentation methods, deep‑learning breakthroughs such as R‑FCN, cascade detectors, corner‑point detection, semantic segmentation, PAnet, and 3‑D point‑cloud approaches, and their impact on accuracy and production efficiency.

Computer VisionDeep Learningground marker recognition
0 likes · 17 min read
How AI Powers Ground Marker Recognition for High‑Precision Maps
iQIYI Technical Product Team
iQIYI Technical Product Team
Jan 9, 2020 · Artificial Intelligence

Results and Winning Solutions of the 2019 CCF Big Data & Computing Intelligence Contest – Video Copyright Detection Track

The 2019 CCF Big Data & Computing Intelligence Contest’s Video Copyright Detection track, judged by iQIYI, saw 705 teams from 25 countries compete, with Hengyang Data’s VGG‑16‑based solution winning, followed by Boyun Vision, Xiao Jia’s Lao Liang, Hulu Brothers and Beihang University, showcasing diverse deep‑learning and unsupervised approaches for robust video copyright detection.

Artificial IntelligenceCCF ContestComputer Vision
0 likes · 9 min read
Results and Winning Solutions of the 2019 CCF Big Data & Computing Intelligence Contest – Video Copyright Detection Track
iQIYI Technical Product Team
iQIYI Technical Product Team
Jan 9, 2020 · Artificial Intelligence

Voice Conversion (VC): Fundamentals, Progress, and Applications

Voice conversion (VC) technology changes a speaker’s timbre and style while keeping the spoken text unchanged, supporting one‑to‑one, many‑to‑one, and many‑to‑many scenarios for medical assistance and entertainment, using parallel or non‑parallel data through methods such as DTW‑aligned frame mapping, attention‑based neural networks, PPG‑LSTM pipelines, VAEs, normalizing‑flow models, and GANs, with iQIYI focusing on non‑parallel data, prosody preservation, and noise‑robust augmentation.

Artificial IntelligenceAudio ProcessingDeep Learning
0 likes · 12 min read
Voice Conversion (VC): Fundamentals, Progress, and Applications
Alibaba Cloud Developer
Alibaba Cloud Developer
Jan 3, 2020 · Artificial Intelligence

How Alibaba’s DAMO Lab Revolutionizes Image Cutout with AI‑Powered Matting

Alibaba's DAMO Academy details its AI‑driven image cutout system, describing why automated matting is needed, the four‑module pipeline (filtering, classification, detection, segmentation), architectural innovations such as dual decoders and fusion networks, and how these advances enable product‑level batch background removal.

AIAlibabaComputer Vision
0 likes · 9 min read
How Alibaba’s DAMO Lab Revolutionizes Image Cutout with AI‑Powered Matting
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
iQIYI Technical Product Team
iQIYI Technical Product Team
Dec 20, 2019 · Artificial Intelligence

Advertising Inventory Forecasting Using an LSTM-Based Deep Learning Model

The iQIYI advertising team introduced an LSTM‑based deep‑learning model that forecasts inventory by normalizing data, clustering dimensions, and embedding fine‑grained holiday features, achieving significantly lower bias than their Adaptive‑ARIMA baseline and improving generalization while reducing training resources.

Advertising ForecastingDeep LearningLSTM
0 likes · 10 min read
Advertising Inventory Forecasting Using an LSTM-Based Deep Learning Model
360 Quality & Efficiency
360 Quality & Efficiency
Dec 20, 2019 · Artificial Intelligence

Automated APK Test Script Recommendation: Data Processing and Model Training Pipeline

This article describes a complete pipeline for recommending automated test scripts for APK releases, covering CSV data preprocessing, feature encoding, tokenization with pkuseg and jieba, and training various machine‑learning models such as LDA, word2vec, XGBoost, deep neural networks, and multi‑label classifiers to predict script execution order.

APK testingDeep LearningModel Training
0 likes · 14 min read
Automated APK Test Script Recommendation: Data Processing and Model Training Pipeline
DataFunTalk
DataFunTalk
Dec 16, 2019 · Artificial Intelligence

A Comprehensive Overview of Sequential Recommendation Models and Techniques

This article provides an in-depth overview of sequential recommendation, defining the problem, discussing data preparation, and reviewing various neural architectures—including MLP, CNN, RNN, Temporal CNN, self‑attention, and reinforcement‑learning approaches—while offering practical guidance on model selection and implementation.

CNNDeep LearningRNN
0 likes · 36 min read
A Comprehensive Overview of Sequential Recommendation Models and Techniques
Amap Tech
Amap Tech
Dec 13, 2019 · Artificial Intelligence

Image Segmentation for High-Definition Mapping: Evolution and Practices at Gaode Maps

Gaode Maps has progressed image segmentation from early heuristic region splitting to modern deep‑learning pipelines—leveraging FCNs, multi‑task networks, Mask R‑CNN, and specialized losses—to achieve centimeter‑level, instance‑aware mapping of roads, signs, and small objects while pursuing lighter, real‑time models.

AIComputer VisionDeep Learning
0 likes · 14 min read
Image Segmentation for High-Definition Mapping: Evolution and Practices at Gaode Maps
DataFunTalk
DataFunTalk
Dec 13, 2019 · Artificial Intelligence

Fundamentals of Deep Learning: Neural Networks, CNNs, RNNs, LSTM, and GRU

This article provides a comprehensive overview of deep learning fundamentals, covering neural network basics, forward and backward feedback architectures, key models such as MLP, CNN, RNN, LSTM and GRU, training techniques like gradient descent, learning rate schedules, momentum, weight decay, and batch normalization.

CNNDeep LearningGRU
0 likes · 14 min read
Fundamentals of Deep Learning: Neural Networks, CNNs, RNNs, LSTM, and GRU