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277 articles · Page 3 of 3
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Jun 12, 2023 · Artificial Intelligence

Comprehensive Guide to Using OpenAI APIs: Models, Prompts, Embeddings, Fine‑Tuning, LangChain, and Multimodal Applications

This article provides a detailed, step‑by‑step tutorial on OpenAI’s language models, API endpoints, prompt engineering, embeddings, moderation, fine‑tuning, LangChain workflows, memory management, and multimodal capabilities such as audio transcription and image generation, complete with code examples and practical usage tips.

APIEmbeddingFine-tuning
0 likes · 45 min read
Comprehensive Guide to Using OpenAI APIs: Models, Prompts, Embeddings, Fine‑Tuning, LangChain, and Multimodal Applications
WeChat Backend Team
WeChat Backend Team
Jun 7, 2023 · Artificial Intelligence

How TransE+ Boosts Knowledge Graph Embedding on WeChat’s Plato Framework

This article presents the development and deployment of the TransE+ knowledge‑graph embedding model on the Plato graph‑computing platform, detailing its architectural upgrades, training optimizations, performance gains, and business‑oriented adaptations for large‑scale real‑world applications.

AIEmbeddingKnowledge Graph
0 likes · 22 min read
How TransE+ Boosts Knowledge Graph Embedding on WeChat’s Plato Framework
Architect
Architect
May 22, 2023 · Artificial Intelligence

Building a ChatGPT‑Powered Markdown Documentation System with Embedbase and Nextra

This article explains step‑by‑step how to turn a static Markdown documentation site into an AI‑enhanced, interactive knowledge base by storing content in Embedbase, retrieving semantically similar passages, constructing context‑aware prompts, and invoking ChatGPT through a custom Nextra search component.

AIChatGPTEmbedding
0 likes · 20 min read
Building a ChatGPT‑Powered Markdown Documentation System with Embedbase and Nextra
Ctrip Technology
Ctrip Technology
May 18, 2023 · Artificial Intelligence

LSTM‑Based Advertising Inventory Forecasting with Embedding and Incremental Training at Ctrip

This article presents Ctrip's end‑to‑end solution for precise ad‑inventory forecasting using an LSTM model combined with entity embedding, covering data preprocessing, K‑means clustering, model architecture, offline‑online incremental training, early‑stop mechanisms, evaluation metrics, and Python service deployment.

EmbeddingLSTMPyTorch
0 likes · 19 min read
LSTM‑Based Advertising Inventory Forecasting with Embedding and Incremental Training at Ctrip
DataFunTalk
DataFunTalk
May 13, 2023 · Artificial Intelligence

Multimedia Content Understanding at Weibo: Video Summarization, Quality Assessment, OCR, Embedding, and CV‑CUDA Optimization

This article presents Weibo's comprehensive multimedia content understanding pipeline, covering video summarization techniques, quality assessment models, OCR advancements, video embedding strategies, and the performance benefits of CV‑CUDA acceleration, while highlighting real‑world applications and engineering trade‑offs.

CV-CUDAEmbeddingOCR
0 likes · 32 min read
Multimedia Content Understanding at Weibo: Video Summarization, Quality Assessment, OCR, Embedding, and CV‑CUDA Optimization
Tencent Advertising Technology
Tencent Advertising Technology
Nov 17, 2022 · Artificial Intelligence

Scaling Huge Embedding Model Training with Cache-Enabled Distributed Framework (HET): VLDB 2022 Best Paper and Its Industrial Deployment

The award‑winning VLDB 2022 paper introduces HET, a cache‑enabled distributed framework that dramatically reduces communication overhead for sparse trillion‑parameter embedding models, and Tencent Ads has industrialized this technology to train 10 TB‑scale models with up to 7×24‑hour online deep learning.

Embeddingcachedeep learning
0 likes · 9 min read
Scaling Huge Embedding Model Training with Cache-Enabled Distributed Framework (HET): VLDB 2022 Best Paper and Its Industrial Deployment
DataFunTalk
DataFunTalk
Oct 31, 2022 · Artificial Intelligence

NVIDIA Merlin HugeCTR: System Overview, Architecture, and Performance

This article introduces NVIDIA Merlin's HugeCTR recommendation system framework, covering its three main modules—NV Tabular, HugeCTR, and Triton—detailing model‑parallel embedding handling, CUDA kernel fusion, mixed‑precision training, hierarchical parameter server inference, Sparse Operation Kit for TensorFlow, performance benchmarks, and practical deployment considerations.

EmbeddingGPU AccelerationHugeCTR
0 likes · 19 min read
NVIDIA Merlin HugeCTR: System Overview, Architecture, and Performance
Alimama Tech
Alimama Tech
Oct 19, 2022 · Artificial Intelligence

Understanding the One-Epoch Overfitting Phenomenon in Deep Click-Through Rate Models

The study reveals that industrial deep click‑through‑rate models often overfit dramatically after the first training epoch—a “one‑epoch phenomenon” caused by the embedding‑plus‑MLP architecture, fast optimizers, and highly sparse features, with performance dropping sharply unless sparsity is reduced or training is limited to a single pass.

CTREmbeddingMLP
0 likes · 15 min read
Understanding the One-Epoch Overfitting Phenomenon in Deep Click-Through Rate Models
ELab Team
ELab Team
Sep 24, 2022 · Frontend Development

Building a Tiny Custom JavaScript Runtime with Duktape and WebAssembly

This article explains how to create a lightweight, embeddable JavaScript runtime using Duktape, compile it to WebAssembly, expose custom APIs, and integrate it into a web-based login greeter, highlighting implementation steps, code examples, and potential use cases.

DuktapeEmbeddingFrontend
0 likes · 8 min read
Building a Tiny Custom JavaScript Runtime with Duktape and WebAssembly
Zhuanzhuan Tech
Zhuanzhuan Tech
Sep 21, 2022 · Artificial Intelligence

Vector Retrieval and Product Quantization with Faiss

This article explains the challenges of large‑scale vector retrieval, compares Faiss index types such as brute‑force, graph‑based and product quantization, and details how product quantization works, its memory‑speed trade‑offs, hierarchical quantization, and practical hyper‑parameter tuning.

ANNEmbeddingFAISS
0 likes · 9 min read
Vector Retrieval and Product Quantization with Faiss
HelloTech
HelloTech
Sep 2, 2022 · Artificial Intelligence

Search and Recommendation Algorithms: Evolution, Common Pipelines, and Integrated Engine Design

The article outlines how search and recommendation systems have evolved from simple hot‑list displays to sophisticated, data‑driven pipelines comprising recall, fine‑ranking and re‑ranking stages, describes an integrated low‑code engine with standardized features, configurable components and intelligent modules that enable rapid deployment across many scenarios, delivering notable CTR, GMV and engagement gains at 哈啰.

EmbeddingRankingalgorithm architecture
0 likes · 10 min read
Search and Recommendation Algorithms: Evolution, Common Pipelines, and Integrated Engine Design
DataFunSummit
DataFunSummit
Sep 1, 2022 · Artificial Intelligence

Temporal Knowledge Graph Question Answering: The TSQA Approach and Experimental Evaluation

This article presents a comprehensive overview of temporal knowledge graphs, outlines the challenges of building question‑answering systems over them, introduces the TSQA method with its three‑step pipeline for time‑sensitive reasoning, and reports experimental results showing significant improvements on complex queries.

EmbeddingTSQATemporal Knowledge Graphs
0 likes · 22 min read
Temporal Knowledge Graph Question Answering: The TSQA Approach and Experimental Evaluation
DataFunSummit
DataFunSummit
Jul 14, 2022 · Artificial Intelligence

Next‑Generation Song Recognition: From Audio Fingerprints to Cover Detection

This article reviews the limitations of traditional audio‑fingerprint song identification, surveys the evolution of cover‑song detection techniques, and details Tencent Music’s Lyra‑CoverNet system—including embedding extraction, sequence retrieval, automated labeling, deployment results, and future research directions—demonstrating how deep learning advances enable more accurate and scalable music recognition.

EmbeddingTencent Musicaudio fingerprint
0 likes · 10 min read
Next‑Generation Song Recognition: From Audio Fingerprints to Cover Detection
DataFunTalk
DataFunTalk
Jul 9, 2022 · Artificial Intelligence

User Behavior Sequence Based Transaction Anti‑Fraud Detection

This presentation explains how leveraging user behavior sequences with supervised and unsupervised deep learning models, including end‑to‑end and two‑stage architectures, improves transaction fraud detection by identifying distinct patterns of account takeover and stolen‑card activities and outlines the engineering deployment pipeline.

EmbeddingSupervised LearningUser Behavior
0 likes · 12 min read
User Behavior Sequence Based Transaction Anti‑Fraud Detection
Python Programming Learning Circle
Python Programming Learning Circle
Jul 4, 2022 · Artificial Intelligence

Building an Advertising Recommendation Model with Python and PyTorch

This article walks through the development of a simple advertising recommendation system using Python, covering data collection, preprocessing with label encoding, text embedding via Torch, constructing an MLP model, and initiating training, while reflecting on the challenges faced by Python developers in the big‑data era.

EmbeddingMLPPyTorch
0 likes · 5 min read
Building an Advertising Recommendation Model with Python and PyTorch
DataFunSummit
DataFunSummit
May 18, 2022 · Artificial Intelligence

Automated Knowledge Graph Representation Learning: From Triples to Subgraphs

This talk introduces automated knowledge graph representation learning, covering background, key techniques such as triple‑based, path‑based and subgraph‑based models, AutoML‑driven model search (AutoSF, Interstellar, RED‑GNN), evaluation metrics, and future research directions in AI.

AutoMLEmbeddingKnowledge Graph
0 likes · 21 min read
Automated Knowledge Graph Representation Learning: From Triples to Subgraphs
DataFunTalk
DataFunTalk
May 8, 2022 · Artificial Intelligence

Automated Knowledge Graph Representation Learning: From Triples to Subgraphs

This talk introduces the background, key directions, and model designs for automated knowledge‑graph representation learning, covering triple‑based, path‑based, and subgraph‑based approaches, the role of AutoML in searching optimal bilinear scoring functions, and future research challenges such as scalability, inductive inference, and domain‑specific applications.

AutoMLEmbeddingKnowledge Graph
0 likes · 20 min read
Automated Knowledge Graph Representation Learning: From Triples to Subgraphs
DataFunSummit
DataFunSummit
May 7, 2022 · Artificial Intelligence

Advances in Click‑Through Rate Prediction: Model Evolution, Feature Interaction, Continuous Feature Embedding, and Distributed Training

This article reviews the development of CTR prediction models from early collaborative‑filtering methods to modern deep‑learning approaches, discusses core challenges such as feature interaction and continuous‑feature embedding, introduces recent Huawei solutions like AutoDis and ScaleFreeCTR for efficient large‑embedding training, and outlines future research directions.

Distributed TrainingEmbeddingRecommendation Systems
0 likes · 21 min read
Advances in Click‑Through Rate Prediction: Model Evolution, Feature Interaction, Continuous Feature Embedding, and Distributed Training
DataFunTalk
DataFunTalk
May 4, 2022 · Artificial Intelligence

Advances in Recommendation Models: CTR Prediction, Continuous Feature Embedding, Interaction Modeling, and Distributed Training

This article reviews the evolution of recommendation models from early collaborative filtering to modern deep learning approaches, discusses core challenges such as CTR prediction, outlines user‑behavior and combination‑feature modeling techniques, introduces large‑embedding training and continuous‑feature embedding methods like AutoDis, and presents distributed training frameworks such as ScaleFreeCTR, concluding with future research directions.

CTR predictionEmbeddingdeep learning
0 likes · 21 min read
Advances in Recommendation Models: CTR Prediction, Continuous Feature Embedding, Interaction Modeling, and Distributed Training
NetEase Cloud Music Tech Team
NetEase Cloud Music Tech Team
Apr 27, 2022 · Artificial Intelligence

How Model-Agnostic Interest Learning (MAIL) Solves Cold‑Start in Recommender Systems

This paper introduces MAIL, a model‑agnostic dual‑tower framework that uses a zero‑shot learning tower to generate virtual user behaviors for new users and an embedding‑based ranking tower, achieving 13‑15% CTR lift in large‑scale live‑stream recommendation at NetEase Cloud Music.

EmbeddingIndustry Insightscold start
0 likes · 33 min read
How Model-Agnostic Interest Learning (MAIL) Solves Cold‑Start in Recommender Systems
DataFunTalk
DataFunTalk
Apr 21, 2022 · Artificial Intelligence

Solving Cold‑Start in Recommender Systems: The DropoutNet Approach

This article explains why cold‑start is a critical challenge for recommender systems, outlines four practical strategies—generalization, fast data collection, transfer learning, and few‑shot learning—and then details the DropoutNet model, its end‑to‑end training, loss functions, negative‑sampling techniques, and open‑source implementation.

DropoutNetEmbeddingTransfer Learning
0 likes · 21 min read
Solving Cold‑Start in Recommender Systems: The DropoutNet Approach
NetEase Media Technology Team
NetEase Media Technology Team
Apr 11, 2022 · Artificial Intelligence

Multimodal Video Tagging: Challenges and a Two‑Stage Recall‑Ranking Solution

To tackle the massive, multimodal tagging challenge of short‑video platforms—characterized by a huge long‑tail tag set, sparse annotations, and uneven modality contributions—the authors propose a two‑stage recall‑ranking system that first retrieves candidates via text, visual, audio and classification cues, then refines them with contrastive learning and extensive hard‑negative sampling, achieving 0.884 tag accuracy in a real‑world news video recommender.

EmbeddingRecommendation Systemsmultimodal learning
0 likes · 12 min read
Multimodal Video Tagging: Challenges and a Two‑Stage Recall‑Ranking Solution
DataFunSummit
DataFunSummit
Apr 2, 2022 · Artificial Intelligence

Graph-Based I2I Recall for Short Video Recommendation at Kuaishou

This article presents Kuaishou's graph‑based item‑to‑item (I2I) recall pipeline for short‑video recommendation, detailing the business challenges, pipeline architecture, optimization techniques such as similarity‑measure tricks, graph structure learning, edge‑weight learning, and future research directions.

AIEmbeddingGraph Neural Network
0 likes · 16 min read
Graph-Based I2I Recall for Short Video Recommendation at Kuaishou
Kuaishou Tech
Kuaishou Tech
Mar 23, 2022 · Artificial Intelligence

Graph-Based I2I Recall for Short Video Recommendation at Kuaishou

This article explains how Kuaishou leverages graph neural networks for item‑to‑item (I2I) recall in short‑video recommendation, detailing the system background, pipeline architecture, optimization techniques such as similarity measurement, graph structure learning, edge‑weight learning, and future research directions.

AIEmbeddingI2I recall
0 likes · 17 min read
Graph-Based I2I Recall for Short Video Recommendation at Kuaishou
DataFunSummit
DataFunSummit
Mar 6, 2022 · Artificial Intelligence

The Evolution of Embedding Techniques: From Word2Vec to Graph Neural Networks

This article traces the development of embedding methods—from the early word2vec model through item2vec, DeepWalk, Node2vec, EGES, HERec, GraphRT, and target‑fitting approaches like DSSM and YouTube recommendation—highlighting how sequence‑construction and target‑fitting paradigms have shaped modern recommendation systems and AI applications.

EmbeddingItem2VecRecommendation Systems
0 likes · 26 min read
The Evolution of Embedding Techniques: From Word2Vec to Graph Neural Networks
Kuaishou Tech
Kuaishou Tech
Feb 24, 2022 · Artificial Intelligence

Causal Inference for Bias Mitigation in Kuaishou Recommendation Systems

This article presents a comprehensive overview of how causal inference techniques are applied to identify and correct various biases in Kuaishou's recommendation pipeline, covering background theory, recent research, practical implementations such as popularity debias, causal embedding decoupling, and video completion‑rate debias, along with experimental results and future challenges.

Bias MitigationEmbeddingKuaishou
0 likes · 19 min read
Causal Inference for Bias Mitigation in Kuaishou Recommendation Systems
DataFunSummit
DataFunSummit
Feb 21, 2022 · Artificial Intelligence

Advances in E‑commerce Search: Embedding, Knowledge Graphs, and Retrieval Models

This article reviews recent research on e‑commerce search, covering transformer‑based complementary rankings, Alibaba's cognitive concept net and its extension, joint deep retrieval with product quantization, personalized semantic retrieval, multi‑granularity deep semantic retrieval, and graph‑attention networks for long‑tail shop search.

AIEmbeddingGraph Neural Network
0 likes · 12 min read
Advances in E‑commerce Search: Embedding, Knowledge Graphs, and Retrieval Models
Baobao Algorithm Notes
Baobao Algorithm Notes
Dec 15, 2021 · Artificial Intelligence

Why Can BERT’s Token, Segment, and Position Embeddings Be Added? A Deep Dive into Positional Encoding

This article revisits the long‑standing question of why BERT’s token, segment, and position embeddings are summed, critiques earlier explanations, and presents findings from the ICLR‑2021 paper “Rethinking Positional Encoding in Language Pre‑training” that show removing the token‑position cross term speeds convergence and improves downstream GLUE scores.

BERTEmbeddingLanguage Pretraining
0 likes · 6 min read
Why Can BERT’s Token, Segment, and Position Embeddings Be Added? A Deep Dive into Positional Encoding
DataFunSummit
DataFunSummit
Nov 19, 2021 · Artificial Intelligence

Sliding Spectrum Decomposition (SSD) for Diversified Recommendation in Re‑ranking

This article reviews the Sliding Spectrum Decomposition (SSD) model presented by Xiaohongshu at KDD 2021, explaining how it incorporates sliding‑window diversity into the re‑ranking stage, combines content‑based and collaborative‑filtering embeddings via the CB2CF framework, and demonstrates its effectiveness through offline and online A/B experiments.

DiversityEmbeddingRe‑ranking
0 likes · 14 min read
Sliding Spectrum Decomposition (SSD) for Diversified Recommendation in Re‑ranking
Alimama Tech
Alimama Tech
Nov 17, 2021 · Artificial Intelligence

Adaptive Masked Twins-based Layer for Efficient Embedding Dimension Selection in Deep Recommendation Models

AMTL inserts an adaptively‑learned twin‑network mask after each representation layer to prune unnecessary embedding dimensions per feature value, automatically assigning larger sizes to high‑frequency features, achieving higher CTR accuracy, about 60% storage reduction, and seamless hot‑starting across recommendation models.

EmbeddingRecommendation Systemsadaptive masking
0 likes · 15 min read
Adaptive Masked Twins-based Layer for Efficient Embedding Dimension Selection in Deep Recommendation Models
Alimama Tech
Alimama Tech
Nov 17, 2021 · Artificial Intelligence

Low‑Carbon Model Compression for Alibaba Mama Search Advertising CTR: Feature Volume and Embedding Dimension Optimizations

The article details Alibaba’s low‑carbon CTR model slimming, showing how binary‑code hash embeddings compress massive feature volumes while the Adaptive‑Masked Twins‑based Layer dynamically reduces embedding dimensions, together cutting storage and compute, lowering collisions, and preserving accuracy for large‑scale search advertising.

CTREmbeddingfeature volume
0 likes · 11 min read
Low‑Carbon Model Compression for Alibaba Mama Search Advertising CTR: Feature Volume and Embedding Dimension Optimizations
Tencent Cloud Developer
Tencent Cloud Developer
Nov 3, 2021 · Backend Development

Using Go as a Scripting Language with Yaegi: Concepts, Quick Start, and Comparative Evaluation

The article explains how Go, traditionally a compiled language, can serve as a scripting language using the Yaegi interpreter—detailing its syntax‑compatible design, easy struct integration, quick‑start example, performance comparison with gopher‑lua and Tengo, and practical engineering guidelines for safe embedding.

EmbeddingGoScripting
0 likes · 16 min read
Using Go as a Scripting Language with Yaegi: Concepts, Quick Start, and Comparative Evaluation
DataFunSummit
DataFunSummit
Nov 2, 2021 · Artificial Intelligence

Applying Deep Learning to Time Series Data for Financial Risk Modeling

This article explains how a financial company leverages deep learning sequence models, including embedding, attention, and transformer techniques, to automatically extract features from massive time‑series data, improve risk model performance, and build a reusable, end‑to‑end system framework.

AIEmbeddingattention
0 likes · 8 min read
Applying Deep Learning to Time Series Data for Financial Risk Modeling
DataFunSummit
DataFunSummit
Oct 31, 2021 · Artificial Intelligence

Exploring Generalized Multi‑Objective Recommendation Algorithms for 58 Community

This article details how 58 Community evolved its recommendation system from single‑objective click‑rate optimization to a multi‑objective framework that boosts value‑content share, improves user retention, and leverages cross‑domain embeddings and online CEM‑based parameter tuning to achieve significant performance gains.

CEMEmbeddingOnline Optimization
0 likes · 15 min read
Exploring Generalized Multi‑Objective Recommendation Algorithms for 58 Community
Ctrip Technology
Ctrip Technology
Oct 28, 2021 · Mobile Development

Embedding Flutter Views in React Native and Native Applications: Architecture, Implementation, and Lessons Learned

This article explores the practical integration of Flutter views within React Native and native mobile pages, detailing architectural choices, lifecycle management, event handling, and code implementations to enable seamless cross‑stack UI composition in a large‑scale travel app.

EmbeddingNativecross‑platform
0 likes · 15 min read
Embedding Flutter Views in React Native and Native Applications: Architecture, Implementation, and Lessons Learned
DataFunTalk
DataFunTalk
Sep 19, 2021 · Artificial Intelligence

Second‑hand Housing Recommendation System: Business Background, Vector Recall, Multi‑objective Optimization and Future Plans

This article presents the end‑to‑end practice of a second‑hand housing recommendation system at 58.com and Anjuke, covering business background, embedding‑based vector recall, multi‑objective ranking methods such as ESMM and MMOE, experimental results, and future development directions.

ESMMEmbeddingFAISS
0 likes · 14 min read
Second‑hand Housing Recommendation System: Business Background, Vector Recall, Multi‑objective Optimization and Future Plans
DataFunSummit
DataFunSummit
Aug 5, 2021 · Artificial Intelligence

Embedding‑Based Item‑to‑Item Similarity Recommendation for Homestay Platforms

This article describes how Tujia applied embedding techniques, inspired by word2vec and skip‑gram models, to build item‑to‑item similarity vectors for homestay recommendations, detailing the background challenges, the embedding solution, training methodology, evaluation results, practical improvements, and future development plans.

AB testingEmbeddinghomestay
0 likes · 13 min read
Embedding‑Based Item‑to‑Item Similarity Recommendation for Homestay Platforms
DataFunTalk
DataFunTalk
Aug 4, 2021 · Artificial Intelligence

Deep Learning Practices for Personalized Recommendation in a Cultural Artifact Auction Platform

This article presents a comprehensive case study of applying deep learning techniques—including item and user embedding, cross‑domain keyword intent modeling, and multi‑interest representation—to improve the recall stage of personalized recommendation for a cultural‑artifact auction platform, addressing unique data sparsity and diversity challenges.

EmbeddingPersonalized Recommendationcross-domain learning
0 likes · 16 min read
Deep Learning Practices for Personalized Recommendation in a Cultural Artifact Auction Platform
DataFunSummit
DataFunSummit
Aug 3, 2021 · Artificial Intelligence

Content Understanding for Personalized Recommendation: Interest Graph, Concept Mining, and Semantic Matching at Tencent

The article explains how Tencent addresses the limitations of traditional content understanding methods in personalized recommendation by introducing an interest‑graph framework that combines classification, concept, entity, and event layers, and details the associated mining, matching, and online evaluation techniques.

EmbeddingNLPcontent understanding
0 likes · 13 min read
Content Understanding for Personalized Recommendation: Interest Graph, Concept Mining, and Semantic Matching at Tencent
DataFunTalk
DataFunTalk
Aug 2, 2021 · Databases

From Text Search to Vector Search: Generalizing Unstructured Data Retrieval

The article explains why traditional text‑based search engines like ElasticSearch struggle with modern multimodal data, introduces vector databases that store implicit semantic embeddings, and proposes a generalized search architecture that decouples data‑to‑vector mapping from the engine while leveraging clustering or graph indexes for similarity search.

AIEmbeddingVector Database
0 likes · 12 min read
From Text Search to Vector Search: Generalizing Unstructured Data Retrieval
DataFunTalk
DataFunTalk
Jul 3, 2021 · Artificial Intelligence

Knowledge Graph Enhanced Recommender Systems: Methods, Models, and Experiments

This article reviews how knowledge graphs can be integrated into recommender systems to address data sparsity and cold‑start problems, covering collaborative filtering limitations, KG embeddings (TransE, TransH, TransR), deep knowledge‑aware networks, multi‑task feature learning, RippleNet, KGCN, experimental results, and a comparative analysis of performance, scalability, and interpretability.

Artificial IntelligenceCollaborative FilteringEmbedding
0 likes · 11 min read
Knowledge Graph Enhanced Recommender Systems: Methods, Models, and Experiments
DataFunTalk
DataFunTalk
Jul 2, 2021 · Artificial Intelligence

Vector Retrieval for Community Forum Search Using Milvus at Dingxiangyuan

This article describes how Dingxiangyuan's algorithm team adopted Milvus for distributed vector indexing to improve semantic search in their community forum, detailing the background, retrieval workflow, various embedding models—including Bi‑Encoder, Spherical Embedding, and Knowledge Embedding—and summarizing the benefits and future applications.

EmbeddingMilvusNLP
0 likes · 10 min read
Vector Retrieval for Community Forum Search Using Milvus at Dingxiangyuan
DataFunTalk
DataFunTalk
Apr 29, 2021 · Artificial Intelligence

Path‑based Deep Network (PDN) for E‑commerce Recommendation Recall

This paper proposes a Path‑based Deep Network (PDN) that combines similarity‑index and embedding‑based retrieval paradigms to model user‑item interactions via Trigger Net and Similarity Net, achieving significant improvements in click‑through rate, GMV, and diversity on Taobao’s homepage feed.

EmbeddingPDNclick-through-rate
0 likes · 21 min read
Path‑based Deep Network (PDN) for E‑commerce Recommendation Recall
DataFunTalk
DataFunTalk
Mar 20, 2021 · Artificial Intelligence

Model‑Based Recall in Momo's Social Recommendation: Technical Exploration and Practical Applications

This article presents a comprehensive technical overview of Momo's model‑based recall system for social recommendation, detailing the underlying user‑scenario behavior models, social graph embeddings, multimodal content semantics, and deployment results that improve matching relevance and user interaction rates.

EmbeddingGraph Neural NetworkMomo
0 likes · 19 min read
Model‑Based Recall in Momo's Social Recommendation: Technical Exploration and Practical Applications
Meituan Technology Team
Meituan Technology Team
Jan 21, 2021 · Mobile Development

Porting Flutter to HarmonyOS: Technical Exploration and Implementation

Meituan’s MTFlutter team rebuilt Flutter’s embedder layer for HarmonyOS by simulating VSync, creating a SurfaceProvider‑based rendering surface, forwarding touch, key and speech events, and re‑implementing asset loading, message loops and lifecycle callbacks, allowing Flutter apps to run on phones, tablets, TVs and wearables.

EmbeddingHarmonyOScross‑platform
0 likes · 13 min read
Porting Flutter to HarmonyOS: Technical Exploration and Implementation
DeWu Technology
DeWu Technology
Jan 18, 2021 · Artificial Intelligence

Recall Stage in Recommendation Systems: From Intuition to Deep Learning

The recall stage, the first filtering step after candidate generation, transforms intuitive attribute‑based shortcuts into sophisticated matrix‑factorization and embedding methods—such as dual‑tower and tree‑based models—enabling fast, personalized, diverse candidate selection for real‑time recommendation pipelines.

Collaborative FilteringEmbeddingRecommendation Systems
0 likes · 13 min read
Recall Stage in Recommendation Systems: From Intuition to Deep Learning
DataFunTalk
DataFunTalk
Jan 7, 2021 · Artificial Intelligence

User Preference Mining and Modeling Practices at Beike

This article introduces the concept of user preference mining, discusses challenges such as accurate expression, interpretability, and high-dimensional preferences, reviews statistical and model-based approaches including weighting, decay, XGBoost, DNN, LSTM, Seq4Rec, and Deep Interest Network, and describes their practical implementation at Beike.

BeikeEmbeddingLSTM
0 likes · 19 min read
User Preference Mining and Modeling Practices at Beike
DataFunTalk
DataFunTalk
Dec 1, 2020 · Artificial Intelligence

A Comprehensive Overview of Embedding Techniques for Recommendation Systems

This article systematically reviews mainstream embedding technologies—including matrix factorization, static and dynamic word embeddings, and graph‑based methods—explaining their principles, implementations, and practical applications in recommendation, advertising, and search systems.

EmbeddingRecommendation Systemsgraph neural networks
0 likes · 32 min read
A Comprehensive Overview of Embedding Techniques for Recommendation Systems
DataFunTalk
DataFunTalk
Nov 28, 2020 · Artificial Intelligence

Building Fast-Iterating Machine Learning Systems at Tubi: A/B Testing, Simple Models, and Embedding Strategies

This article shares Tubi's practical experience in rapidly iterating machine‑learning systems, emphasizing the early importance of simple end‑to‑end A/B testing platforms, clear launch plans, heat‑based and embedding‑based ranking models, and a culture of fast experimentation over complex deep‑learning research.

A/B testingArtificial IntelligenceData Engineering
0 likes · 8 min read
Building Fast-Iterating Machine Learning Systems at Tubi: A/B Testing, Simple Models, and Embedding Strategies
Bitu Technology
Bitu Technology
Nov 20, 2020 · Artificial Intelligence

Building a Model-Driven Machine Learning System at Tubi: From Simple A/B Tests to Embedding-Based Recommendations

The article shares Tubi's practical experience in building a fast‑iterating machine‑learning platform, emphasizing early measurement, simple end‑to‑end A/B testing, clear launch plans, lightweight popularity and embedding models, and rapid experimentation to drive product decisions.

A/B testingArtificial IntelligenceEmbedding
0 likes · 8 min read
Building a Model-Driven Machine Learning System at Tubi: From Simple A/B Tests to Embedding-Based Recommendations
Sohu Tech Products
Sohu Tech Products
Nov 18, 2020 · Artificial Intelligence

Understanding Sequence‑to‑Sequence (seq2seq) Models and Attention Mechanisms

This article explains the fundamentals of seq2seq neural machine translation models, covering encoder‑decoder architecture, word embeddings, context vectors, RNN processing, and the attention mechanism introduced by Bahdanau and Luong, with visual illustrations and reference links for deeper study.

EmbeddingNeural Machine TranslationRNN
0 likes · 11 min read
Understanding Sequence‑to‑Sequence (seq2seq) Models and Attention Mechanisms
58 Tech
58 Tech
Nov 11, 2020 · Artificial Intelligence

Deep Learning for Click‑Through Rate Prediction in 58.com Home‑Page Recommendation

This article details how 58.com leverages deep learning models such as DNN, Wide&Deep, DeepFM, DIN and DIEN, combined with extensive user‑behavior feature engineering, offline vectorization, and online TensorFlow‑Serving pipelines to improve home‑page recommendation click‑through rates and overall platform efficiency.

A/B testingAttention MechanismCTR prediction
0 likes · 25 min read
Deep Learning for Click‑Through Rate Prediction in 58.com Home‑Page Recommendation
DataFunTalk
DataFunTalk
Nov 7, 2020 · Artificial Intelligence

Knowledge Graph Reasoning: Deductive, Inductive, and Embedding‑Based Methods

This article surveys knowledge‑graph reasoning, explaining deductive and inductive reasoning fundamentals, description‑logic and logic‑programming approaches, and modern embedding techniques such as TransE, TransH, TransR and TransD, while highlighting their theoretical bases, practical implementations and recent research progress.

AIDescription LogicEmbedding
0 likes · 13 min read
Knowledge Graph Reasoning: Deductive, Inductive, and Embedding‑Based Methods
DataFunTalk
DataFunTalk
Oct 28, 2020 · Artificial Intelligence

All-Rounder Recall Representation Algorithm Practice

This article presents a comprehensive overview of NetEase Yanxuan’s recall representation algorithms, detailing problem definition, model value, iterative implementations—including session-based embedding, GCN, GraphSAGE, LightGCN, and multi-interest models—along with engineering solutions, performance comparisons, and real-world deployment outcomes in search and recommendation systems.

EmbeddingGraph Neural Networkmachine learning
0 likes · 16 min read
All-Rounder Recall Representation Algorithm Practice
DataFunTalk
DataFunTalk
Aug 27, 2020 · Artificial Intelligence

Model Serving in Real-Time: Insights from Alibaba’s User Interest Center

This article explains Alibaba’s User Interest Center approach to real‑time model serving, detailing how it separates offline sequence modeling from lightweight online inference, uses an online interest‑embedding store, and dramatically reduces latency for recommendation models such as DIEN and MIMN.

AlibabaEmbeddingReal-time inference
0 likes · 8 min read
Model Serving in Real-Time: Insights from Alibaba’s User Interest Center
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.

CtripEmbeddingdeep learning
0 likes · 24 min read
Hotel Recommendation System Architecture, Models, and Evaluation at Ctrip
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.

ClusteringEmbeddingTencent
0 likes · 15 min read
Embedding Techniques in Tencent Mobile News Recommendation System
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.

ElasticsearchEmbeddingTensorFlow Serving
0 likes · 9 min read
How Embedding-Based Recall Boosted Interaction by 33% in a Live Feed
DataFunTalk
DataFunTalk
Jun 10, 2020 · Artificial Intelligence

Embedding Techniques for Real Estate Recommendation at 58.com

This article explains how 58.com applies various embedding methods—including ALS, Skip‑gram, and DeepWalk—to vectorize users and properties, improve similarity calculations, and enhance both recall and ranking stages of its real‑estate recommendation system, with detailed technical descriptions and evaluation results.

ALSDeepWalkEmbedding
0 likes · 16 min read
Embedding Techniques for Real Estate Recommendation at 58.com
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.

AdvertisingDSSMEmbedding
0 likes · 22 min read
Embedding Techniques for Advertising Recall and Ranking in a Second-Hand Car Platform
360 Tech Engineering
360 Tech Engineering
Mar 6, 2020 · Fundamentals

Understanding Method Sets, Interfaces, and Embedding in Go

This article explains Go's method sets, the relationship between method receivers and method sets, how interfaces work, and the role of embedding, providing code examples that illustrate value vs pointer receivers, interface implementation rules, and embedding differences for struct types.

EmbeddingStructinterface
0 likes · 10 min read
Understanding Method Sets, Interfaces, and Embedding in Go
Qunar Tech Salon
Qunar Tech Salon
Feb 6, 2020 · Artificial Intelligence

Content Understanding for Personalized Feed Recommendation: From Classification to Interest Graphs

The article explains how Tencent tackles content understanding in feed recommendation by evolving from traditional classification, keyword, and entity methods to a multi‑layer interest graph that captures concepts and events, addressing the need for full context, reasoning about user intent, and improving online performance.

AIEmbeddingNLP
0 likes · 12 min read
Content Understanding for Personalized Feed Recommendation: From Classification to Interest Graphs
Aotu Lab
Aotu Lab
Dec 5, 2019 · Databases

Mastering One-to-N Relationships in MongoDB: Practical Design Patterns and Tips

This multi‑part guide explains how to model One‑to‑N relationships in MongoDB, covering basic patterns for one‑to‑few, one‑to‑many, and one‑to‑squillions, then advancing to two‑way referencing and denormalization, and finally offering a concise set of rules of thumb for choosing the right schema design.

DenormalizationEmbeddingMongoDB
0 likes · 21 min read
Mastering One-to-N Relationships in MongoDB: Practical Design Patterns and Tips
Tencent Cloud Developer
Tencent Cloud Developer
Dec 3, 2019 · Artificial Intelligence

Feature Engineering Practices for Short‑Video Recommendation Systems

Effective short‑video recommendation relies on meticulous feature engineering that transforms raw signals—numerical counts, categorical IDs, content and user embeddings, context and session data—through bucketization, scaling, crossing, and smoothing, then selects and evaluates them via filtering, wrapping, regularization, and importance analysis to mitigate business biases and improve multi‑objective ranking performance.

Bias MitigationEmbeddingdata preprocessing
0 likes · 32 min read
Feature Engineering Practices for Short‑Video Recommendation Systems
Alibaba Cloud Developer
Alibaba Cloud Developer
Jul 24, 2019 · Artificial Intelligence

Unlocking Better Knowledge Graph Reasoning: The CrossE Model Explained

CrossE introduces an explicit crossover interaction mechanism for knowledge graph embedding, learning both general and interaction-specific representations of entities and relations, which improves link prediction accuracy and provides interpretable explanations, as demonstrated on benchmark datasets WN18, FB15k, and FB15k-237.

EmbeddingKnowledge Graphcrossover interaction
0 likes · 9 min read
Unlocking Better Knowledge Graph Reasoning: The CrossE Model Explained
DataFunTalk
DataFunTalk
Jun 25, 2019 · Artificial Intelligence

Embedding‑Based Item‑to‑Item Recommendation for Homestay Platforms

This article describes how Tujia applied embedding techniques, particularly a Skip‑Gram model, to build an item‑to‑item similarity recommender for low‑frequency, highly personalized homestay listings, detailing the data preparation, model architecture, training process, evaluation results, practical improvements, and future directions.

AB testEmbeddingSkip-gram
0 likes · 13 min read
Embedding‑Based Item‑to‑Item Recommendation for Homestay Platforms
Youku Technology
Youku Technology
Apr 22, 2019 · Artificial Intelligence

Exploring the Construction of an Entertainment Brain: AI and Big Data Practices in the Fish Brain Platform

The talk introduces Alibaba’s Fish Brain platform, an AI‑powered decision‑support system for entertainment that combines a three‑layer data‑model, AI‑processed basic data, and application models, leveraging NLP, computer‑vision, custom embeddings, loss functions and predictive hybrid networks to analyze content, user behavior, and forecast performance.

AIBig DataEmbedding
0 likes · 12 min read
Exploring the Construction of an Entertainment Brain: AI and Big Data Practices in the Fish Brain Platform
NetEase Game Operations Platform
NetEase Game Operations Platform
Mar 27, 2019 · Big Data

Embedding Python in Java with Jython for Real‑Time Big Data Jobs

This article explains why and how to embed Python code in Java using Jython for real‑time big‑data processing, covering performance benefits, memory‑leak pitfalls, singleton interpreter patterns, function factories, Java‑object conversion, and importing external PyPI packages with practical code examples.

Big DataDynamic LanguageEmbedding
0 likes · 11 min read
Embedding Python in Java with Jython for Real‑Time Big Data Jobs
DataFunTalk
DataFunTalk
Mar 19, 2019 · Artificial Intelligence

Using Field-aware FM (FFM) Models for Unified Recall in Recommendation Systems

This article explores how Field-aware Factorization Machines (FFM) can be employed to replace multi‑path recall strategies in industrial recommendation systems, detailing model principles, embedding construction, integration of user, item and context features, performance considerations, and potential for unifying recall and ranking stages.

EmbeddingFFMRecommendation Systems
0 likes · 51 min read
Using Field-aware FM (FFM) Models for Unified Recall in Recommendation Systems
Sohu Tech Products
Sohu Tech Products
Mar 6, 2019 · Artificial Intelligence

Applying Word2Vec Embeddings to Rental and News Recommendation: Model, Hyper‑parameters, and Optimization

This article explains the fundamentals of the Word2Vec SGNS model, details its hyper‑parameters and training tricks, and demonstrates how customized embeddings are built for rental‑listing and news‑article recommendation, covering data preparation, objective‑function redesign, evaluation, and deployment in both recall and ranking stages.

EmbeddingSGNSWord2Vec
0 likes · 14 min read
Applying Word2Vec Embeddings to Rental and News Recommendation: Model, Hyper‑parameters, and Optimization
DataFunTalk
DataFunTalk
Jan 8, 2019 · Artificial Intelligence

Yoo Video Bottom‑Page Recommendation System: From Zero to One Practice

This article details the end‑to‑end design, recall and ranking techniques, engineering implementation, and future research directions of Tencent's Yoo video bottom‑page recommendation system, illustrating how large‑scale video recommendation is built from business needs to deep learning models.

Embeddinglarge-scale systemsmachine learning
0 likes · 13 min read
Yoo Video Bottom‑Page Recommendation System: From Zero to One Practice
JavaScript
JavaScript
Nov 3, 2017 · Frontend Development

How to Embed Web Pages in Mini-Programs Using the Web-View Component

Developers can now flexibly embed web pages within mini-programs using the web-view component, which fills the entire page as a container, though it currently excludes personal and overseas mini-program types, and the guide shows the required WXML markup to set the source URL.

EmbeddingFrontendWeChat
0 likes · 1 min read
How to Embed Web Pages in Mini-Programs Using the Web-View Component