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AntData
AntData
Nov 18, 2024 · Databases

Modern Data Paradigms: From Relational Databases to Vector Retrieval and AI

This article surveys the evolution of modern data technologies—from the 4V characteristics of big data and the limitations of traditional relational databases, through the rise of NoSQL and polyglot persistence, to embedding‑driven vector search, hybrid retrieval and RAG, illustrating how each paradigm frees applications from data constraints.

Big DataData ArchitectureEmbedding
0 likes · 30 min read
Modern Data Paradigms: From Relational Databases to Vector Retrieval and AI
Alibaba Cloud Native
Alibaba Cloud Native
Oct 26, 2024 · Artificial Intelligence

Build a Real‑Time Semantic Search with EventBridge, DashVector, and FunctionCompute

This tutorial walks through constructing a zero‑to‑one RAG pipeline that ingests OSS text files via EventBridge, transforms them into embeddings with DashScope, stores vectors in DashVector, and performs semantic search using FunctionCompute and a Qwen‑Turbo LLM, complete with code samples and configuration steps.

DashVectorEmbeddingEventBridge
0 likes · 10 min read
Build a Real‑Time Semantic Search with EventBridge, DashVector, and FunctionCompute
DataFunSummit
DataFunSummit
Oct 18, 2024 · Artificial Intelligence

Building Efficient RAG Applications with a Small Team: Insights from PingCAP AI Lab

This article details how PingCAP's three‑person AI Lab leveraged Retrieval‑Augmented Generation (RAG) techniques—including basic RAG, fine‑tuned embeddings, re‑ranking, graph RAG, and agent‑based RAG—to create scalable, multilingual document‑question answering services while addressing large‑scale documentation challenges, model limitations, and user feedback loops.

AgentEmbeddingFine-tuning
0 likes · 14 min read
Building Efficient RAG Applications with a Small Team: Insights from PingCAP AI Lab
FunTester
FunTester
Oct 10, 2024 · Backend Development

Unlocking Go's Power: How Goja Brings JavaScript to Your Go Apps

This article explores Goja, a pure‑Go JavaScript engine, detailing its features, integration with the K6 load‑testing tool, and practical code examples for embedding scripts, passing values, handling structs, invoking Go functions, error handling, and VM pooling to achieve high performance in Go applications.

EmbeddingGoGoja
0 likes · 14 min read
Unlocking Go's Power: How Goja Brings JavaScript to Your Go Apps
JD Tech Talk
JD Tech Talk
Oct 8, 2024 · Artificial Intelligence

Building a Retrieval‑Augmented Generation (RAG) System with Rust and Qdrant

This article explains how to construct a Retrieval‑Augmented Generation pipeline in Rust, covering knowledge‑base creation with Qdrant, model loading and embedding using the candle library, data ingestion, and integration of a Rust‑based inference service based on mistral.rs, while also discussing resource usage and common pitfalls.

AIEmbeddingLLM
0 likes · 16 min read
Building a Retrieval‑Augmented Generation (RAG) System with Rust and Qdrant
JavaEdge
JavaEdge
Oct 2, 2024 · Artificial Intelligence

Boost RAG Retrieval Accuracy with Contextual Embeddings and BM25

This article presents a contextual retrieval technique that combines contextual embeddings and contextual BM25 to reduce RAG miss rates by up to 67%, explains the underlying methods, implementation steps, cost considerations, experimental results, and practical deployment guidance.

AIBM25Contextual Retrieval
0 likes · 17 min read
Boost RAG Retrieval Accuracy with Contextual Embeddings and BM25
JD Cloud Developers
JD Cloud Developers
Sep 29, 2024 · Artificial Intelligence

Build a Local AI Q&A System with Java, Ollama, and LangChain4J

This article walks through building a local AI question‑answer system using Java, Ollama, LangChain4J, embeddings, and a Chroma vector database, covering LLM fundamentals, embedding techniques, RAG architecture, setup steps, Maven dependencies, and sample code to retrieve and answer queries.

AIEmbeddingJava
0 likes · 19 min read
Build a Local AI Q&A System with Java, Ollama, and LangChain4J
iQIYI Technical Product Team
iQIYI Technical Product Team
Sep 26, 2024 · Artificial Intelligence

AI-Powered Search in iQIYI: Techniques, Architecture, and Implementation

iQIYI’s AI‑powered search expands beyond title‑only queries by handling fuzzy role, plot, star, award, and semantic searches, using Chain‑of‑Thought‑generated TIPS, Retrieval‑Augmented Generation with sophisticated indexing, chunking, embedding, reranking, and prompt‑engineering to deliver personalized, accurate video recommendations that boost user engagement.

AI searchEmbeddingQuery Guidance
0 likes · 15 min read
AI-Powered Search in iQIYI: Techniques, Architecture, and Implementation
ByteDance Data Platform
ByteDance Data Platform
Sep 25, 2024 · Artificial Intelligence

How LLMs Power the “Find Data Assistant” for Smarter Data Retrieval

This article explains how the Volcano Engine DataLeap team leveraged large‑language models to build the “Find Data Assistant”, detailing its design, challenges, embedding‑and‑reranker enhancements, LLM‑driven semantic search, mixing architecture, and practical lessons for improving data asset management and retrieval.

Data Asset ManagementData RetrievalEmbedding
0 likes · 17 min read
How LLMs Power the “Find Data Assistant” for Smarter Data Retrieval
JavaEdge
JavaEdge
Sep 24, 2024 · Artificial Intelligence

Mastering RAG with LangChain4j: From Simple Setup to Advanced Retrieval‑Augmented Generation

This article explains how to extend large language models with domain‑specific knowledge using Retrieval‑Augmented Generation (RAG) in LangChain4j, covering the concepts of RAG, its indexing and retrieval stages, simple RAG setup, detailed API usage, and advanced customization options such as query transformers and content injectors.

EmbeddingJavaLLM
0 likes · 24 min read
Mastering RAG with LangChain4j: From Simple Setup to Advanced Retrieval‑Augmented Generation
DataFunSummit
DataFunSummit
Sep 21, 2024 · Artificial Intelligence

DataLeap "Find Data Assistant": Leveraging Large Language Models for Data Asset Retrieval and Management

This article details how the DataLeap team applied large language model technology to build the "Find Data Assistant" platform, addressing the challenges of locating and using massive data assets through a hybrid retrieval architecture, enhanced embedding, reranking, mixed ranking, and answer summarization, while sharing practical lessons and future directions.

Data Asset ManagementData RetrievalEmbedding
0 likes · 17 min read
DataLeap "Find Data Assistant": Leveraging Large Language Models for Data Asset Retrieval and Management
Code Mala Tang
Code Mala Tang
Sep 12, 2024 · Artificial Intelligence

Boost LLM Accuracy with Retrieval‑Augmented Generation Using LangChain.js

This article explains the core concepts of Retrieval‑Augmented Generation (RAG), walks through its implementation steps with LangChain.js—including text chunking, embedding, storage, retrieval, and generation—and showcases practical use cases, challenges, and best practices for building reliable AI‑powered applications.

AI applicationsEmbeddingLLM
0 likes · 16 min read
Boost LLM Accuracy with Retrieval‑Augmented Generation Using LangChain.js
Baidu Geek Talk
Baidu Geek Talk
Aug 7, 2024 · Artificial Intelligence

Detecting Time‑Series Anomalies in Embedding Space: A Practical AI Approach

This article presents an embedding‑based method for time‑series anomaly detection in security and anti‑cheat scenarios, explains how to vectorise logs, sample and compute distribution features, details implementation code, and validates the approach with four synthetic experiments showing precision‑recall improvements at day and hour granularity.

EmbeddingSecurityTime Series
0 likes · 12 min read
Detecting Time‑Series Anomalies in Embedding Space: A Practical AI Approach
37 Interactive Technology Team
37 Interactive Technology Team
Aug 5, 2024 · Artificial Intelligence

Case Study: Applying AIGC to Component Activity Business with Dify

This case study shows how AIGC, implemented through Dify’s low‑code platform, enables a natural‑language AI assistant to recommend and insert the optimal components from a 200‑plus library, streamlining selection, building an embedding‑based knowledge base, exposing a RAG‑driven agent via API, and demonstrating rapid AI‑business validation compared with custom frameworks.

AI AgentAIGCBusiness Automation
0 likes · 8 min read
Case Study: Applying AIGC to Component Activity Business with Dify
Tencent Advertising Technology
Tencent Advertising Technology
Jul 17, 2024 · Artificial Intelligence

Ads Recommendation in a Collapsed and Entangled World: Tencent's Innovations in Feature Encoding, Dimensional Collapse Mitigation, and Interest Disentanglement

This article summarizes Tencent Advertising's recent research on recommendation models, covering comprehensive feature encoding techniques, solutions to embedding dimensional collapse through multi‑embedding paradigms, and novel methods such as STEM and AME to disentangle conflicting user interests across multiple tasks.

AdvertisingEmbeddingdimensional collapse
0 likes · 20 min read
Ads Recommendation in a Collapsed and Entangled World: Tencent's Innovations in Feature Encoding, Dimensional Collapse Mitigation, and Interest Disentanglement
JD Tech
JD Tech
Jul 10, 2024 · Artificial Intelligence

Implementing Retrieval‑Augmented Generation (RAG) with LangChain4j in Java

This article provides a step‑by‑step guide for Java engineers on building a Retrieval‑Augmented Generation (RAG) application using the LangChain4j framework, covering RAG fundamentals, environment setup, Maven integration, document loading, splitting, embedding with OpenAI, vector store management with Chroma, and prompt‑based LLM interaction.

EmbeddingJavaLLM
0 likes · 35 min read
Implementing Retrieval‑Augmented Generation (RAG) with LangChain4j in Java
Data Thinking Notes
Data Thinking Notes
Jun 20, 2024 · Artificial Intelligence

Leveraging LLMs for Data: Embedding Search, Knowledge Bases, Text2SQL, and EDA

This article explores how large language models can transform data workflows by using embeddings for semantic search, building private domain knowledge bases, generating SQL code from natural language with visualized results, and enhancing exploratory data analysis, outlining practical steps and benefits for enterprises.

EDAEmbeddingKnowledge Base
0 likes · 7 min read
Leveraging LLMs for Data: Embedding Search, Knowledge Bases, Text2SQL, and EDA
JD Cloud Developers
JD Cloud Developers
Jun 20, 2024 · Artificial Intelligence

How Large Language Models Boost Courier Efficiency: From Voice Commands to Smart QA

This article explains how large language models like ChatGPT can transform courier operations by automating voice‑driven tasks, enabling intelligent question answering with retrieval‑augmented generation, extracting and splitting document content, embedding it for vector search, and delivering smart prompts and agents to improve productivity and accuracy.

AIEmbeddingLogistics
0 likes · 15 min read
How Large Language Models Boost Courier Efficiency: From Voice Commands to Smart QA
JD Retail Technology
JD Retail Technology
Jun 4, 2024 · Databases

How to Deploy and Query JD’s Open‑Source Vearch Vector Database for LLM Retrieval

This article walks through the practical use of JD’s self‑developed Vearch vector database—covering cluster creation, space setup, data insertion, and both text and vector search—illustrating how it integrates with LangChain and OpenAI embeddings to enable retrieval‑augmented generation for large language models.

EmbeddingLLM RetrievalLangChain
0 likes · 16 min read
How to Deploy and Query JD’s Open‑Source Vearch Vector Database for LLM Retrieval
DataFunSummit
DataFunSummit
Jun 4, 2024 · Artificial Intelligence

Multimodal and Graph Neural Network Techniques for eBay Recommendation Systems

This article details eBay's practical experience integrating multimodal data and graph neural networks into its recommendation pipeline, covering pain‑point analysis, a twin‑tower multimodal embedding model with triplet loss and TransH, engineering design, experimental results, and key takeaways for future AI‑driven product development.

EmbeddingGNNGraph Neural Network
0 likes · 19 min read
Multimodal and Graph Neural Network Techniques for eBay Recommendation Systems
37 Interactive Technology Team
37 Interactive Technology Team
May 27, 2024 · Artificial Intelligence

Enhancing AI Code Review Quality with Contextual Embedding and Function Calling

The article explains how AI code reviews suffer from missing context, and improves them by embedding the codebase, using Retrieval‑Augmented Generation to fetch relevant snippets, and adding a function‑calling tool that lets the model autonomously request additional code, resulting in precise, bug‑detecting feedback.

AI code reviewEmbeddingFunction Calling
0 likes · 8 min read
Enhancing AI Code Review Quality with Contextual Embedding and Function Calling
NewBeeNLP
NewBeeNLP
May 24, 2024 · Artificial Intelligence

How NoteLLM Boosts Cold‑Start Recommendation with Generative Contrastive Learning

This article reviews the NoteLLM paper, which leverages Llama 2 to create richer text embeddings and automatically generate tags and categories for note recommendation, addressing cold‑start issues through a multitask prompt design, generative‑contrastive learning, and collaborative supervised fine‑tuning, and demonstrates strong offline and online gains.

EmbeddingGenerative Contrastive LearningLLM
0 likes · 14 min read
How NoteLLM Boosts Cold‑Start Recommendation with Generative Contrastive Learning
DataFunTalk
DataFunTalk
May 9, 2024 · Artificial Intelligence

Graph Model Practices and Applications in Baidu Recommendation System

This article introduces the background of graph data, explains common graph modeling algorithms such as graph embedding and graph neural networks, compares their strengths, and details the evolution and large‑scale deployment of Feed graph models in Baidu's recommendation platform.

BaiduEmbeddingRecommendation Systems
0 likes · 11 min read
Graph Model Practices and Applications in Baidu Recommendation System
JD Tech
JD Tech
Apr 18, 2024 · Artificial Intelligence

Getting Started with LangChain: Overview, Core Components, and Python Code Samples

This article introduces the LangChain framework for large language model integration, explains its key components and advantages, and provides step‑by‑step Python examples for setting up environment variables, creating prompts, chaining models, and using embeddings, completions, and chat models.

ChatModelEmbeddingLLM
0 likes · 7 min read
Getting Started with LangChain: Overview, Core Components, and Python Code Samples
JD Tech
JD Tech
Apr 3, 2024 · Backend Development

Understanding JavaScript Engines: QuickJS, V8, TurboFan, and Integration with libuv

This article provides a comprehensive technical overview of JavaScript language standards, the architecture and optimization techniques of major JavaScript engines such as V8 and TurboFan, introduces the lightweight QuickJS engine with its features, file layout and memory management, and demonstrates how to embed QuickJS with libuv for asynchronous I/O using detailed C code examples.

C APIEmbeddingEngine
0 likes · 15 min read
Understanding JavaScript Engines: QuickJS, V8, TurboFan, and Integration with libuv
Sohu Tech Products
Sohu Tech Products
Mar 27, 2024 · Artificial Intelligence

Building a RAG Application with Baidu Vector Database and Qianfan Embedding

This tutorial walks through building a Retrieval‑Augmented Generation application by setting up Baidu’s Vector Database and Qianfan embedding service, configuring credentials, creating a document database and vector table, loading and chunking PDFs, generating embeddings, storing them, and performing scalar, vector and hybrid similarity searches, ready for integration with Wenxin LLM for answer generation.

AI applicationsBaidu QianfanEmbedding
0 likes · 11 min read
Building a RAG Application with Baidu Vector Database and Qianfan Embedding
Eric Tech Circle
Eric Tech Circle
Mar 24, 2024 · Artificial Intelligence

Running Local LLMs: Ollama vs Hugging Face – A Hands‑On Comparison

This guide compares Ollama and Hugging Face for running large language models locally, detailing API and local execution methods, installation steps, model selection, resource requirements, integration with AnythingLLM, container deployment, embedding and vector store setup, and practical observations on performance and limitations.

AnythingLLMDockerEmbedding
0 likes · 15 min read
Running Local LLMs: Ollama vs Hugging Face – A Hands‑On Comparison
Sohu Tech Products
Sohu Tech Products
Mar 13, 2024 · Artificial Intelligence

Build a Minimal Retrieval‑Augmented Generation (Tiny‑RAG) from Scratch

This step‑by‑step guide explains how to implement a lightweight Retrieval‑Augmented Generation system—Tiny‑RAG—by creating embedding classes, loading and chunking documents, building a simple vector store, performing similarity search, and integrating a large language model for answer generation, complete with runnable Python code.

EmbeddingLLMPython
0 likes · 14 min read
Build a Minimal Retrieval‑Augmented Generation (Tiny‑RAG) from Scratch
DaTaobao Tech
DaTaobao Tech
Feb 21, 2024 · Artificial Intelligence

An Overview of LangChain: Core Concepts and Practical Implementations

The article introduces LangChain as a framework that unifies LLM providers through model I/O, connects external data via retrievers, composes workflows with chains, maintains context with memory, and enables tool use through agents, and demonstrates Java examples for TongYi embeddings, a ChatGLM‑6B RetrievalQA chain, and discusses agent registration and micro‑service‑based agent factories.

EmbeddingJavaLLM
0 likes · 9 min read
An Overview of LangChain: Core Concepts and Practical Implementations
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Nov 29, 2023 · Artificial Intelligence

Building a Private LLM‑Powered Knowledge Base with LangChain and ChatGLM3

This article explains how to migrate personal notes into a private knowledge base by combining a large language model with an external vector store, detailing the concepts of tokenization, embedding, vector databases, and step‑by‑step deployment using LangChain‑Chatchat and the open‑source ChatGLM3 model.

ChatGLM3EmbeddingKnowledge Base
0 likes · 10 min read
Building a Private LLM‑Powered Knowledge Base with LangChain and ChatGLM3
MaGe Linux Operations
MaGe Linux Operations
Nov 27, 2023 · Fundamentals

Mastering Go Structs: From Definition to Advanced Embedding

This comprehensive guide walks you through Go structs—covering their definition, instantiation, methods, receivers, anonymous fields, embedding, and method expressions—while providing clear code examples and practical guidelines for effective use in Go programming.

BackendEmbeddingGo
0 likes · 9 min read
Mastering Go Structs: From Definition to Advanced Embedding
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Nov 24, 2023 · Artificial Intelligence

Step-by-Step Guide to Deploying LangChain‑Chatchat with ChatGLM‑2 on a Local Machine

This article provides a comprehensive tutorial on setting up the LangChain‑Chatchat open‑source project, covering environment preparation, model and embedding downloads, configuration files, database initialization, API service launch, and example curl commands for interacting with both the large language model and the local knowledge base.

APIChatGLMEmbedding
0 likes · 9 min read
Step-by-Step Guide to Deploying LangChain‑Chatchat with ChatGLM‑2 on a Local Machine
Data Thinking Notes
Data Thinking Notes
Nov 12, 2023 · Artificial Intelligence

Unlocking LLM Power: Semantic Search, Private Knowledge Bases, and Text‑to‑SQL for Data Teams

This article explores how large language models can boost data workflows by using embeddings for semantic retrieval, building domain‑specific knowledge bases for private Q&A, generating SQL code from natural language, and automating exploratory data analysis, offering practical steps and visual examples.

EmbeddingKnowledge BaseLLM
0 likes · 7 min read
Unlocking LLM Power: Semantic Search, Private Knowledge Bases, and Text‑to‑SQL for Data Teams
dbaplus Community
dbaplus Community
Oct 14, 2023 · Artificial Intelligence

Demystifying Retrieval‑Augmented Generation: From Theory to Working Chatbot

This guide explains the Retrieval‑Augmented Generation (RAG) technique, detailing how user queries are matched to private knowledge bases, how relevant passages are retrieved, and how large language models use those passages to generate context‑aware answers, complete with code examples and practical tips.

ChatbotEmbeddingLLM
0 likes · 19 min read
Demystifying Retrieval‑Augmented Generation: From Theory to Working Chatbot
DataFunSummit
DataFunSummit
Oct 3, 2023 · Artificial Intelligence

Time Series Forecasting for NIO Power Swap Stations: Business Background, Challenges, Algorithm Practice, and Future Outlook

This article presents a comprehensive case study of NIO's Power swap‑station ecosystem, detailing the business context, key forecasting challenges, the evolution from classical statistical models to deep‑learning architectures with specialized embeddings, and the practical outcomes and future plans for improving prediction accuracy.

Deep LearningElectric VehicleEmbedding
0 likes · 16 min read
Time Series Forecasting for NIO Power Swap Stations: Business Background, Challenges, Algorithm Practice, and Future Outlook
Alibaba Cloud Developer
Alibaba Cloud Developer
Sep 21, 2023 · Artificial Intelligence

How Vector Search Powers AI: From Embeddings to Real‑World Applications

This article explains how vector search converts unstructured data such as speech, images, video, and text into high‑dimensional embeddings, explores common algorithms like Brute‑Force, ANN, and HNSW, and presents optimization techniques that dramatically improve recall and query‑per‑second performance for large‑scale AI retrieval systems.

AIANNEmbedding
0 likes · 27 min read
How Vector Search Powers AI: From Embeddings to Real‑World Applications
DaTaobao Tech
DaTaobao Tech
Sep 13, 2023 · Artificial Intelligence

Integrating Large Language Models with Recommendation Systems: Paradigms, Methods, and Experiments

The article surveys how large language models can be integrated into recommendation systems, either as feature extractors or as end‑to‑end recommenders, showing that LLM‑derived semantics improve recall, ranking, diversity, and user experience, and outlining future multimodal, efficiency, and re‑ranking directions.

EmbeddingLLMPrompt engineering
0 likes · 19 min read
Integrating Large Language Models with Recommendation Systems: Paradigms, Methods, and Experiments
ZhongAn Tech Team
ZhongAn Tech Team
Sep 4, 2023 · Artificial Intelligence

Embedding Technology for FAQ Retrieval: Cases, Evaluation Metrics, and Model Comparison

This article introduces the evolution of embedding techniques, presents real‑world case studies of embedding‑based FAQ retrieval, explains evaluation metrics such as Recall and MRR, and compares the performance of a proprietary ZhongAn embedding model with OpenAI and Sentence‑BERT models on Chinese FAQ datasets.

EmbeddingEvaluation MetricsFAQ Retrieval
0 likes · 18 min read
Embedding Technology for FAQ Retrieval: Cases, Evaluation Metrics, and Model Comparison
Architect
Architect
Aug 31, 2023 · Artificial Intelligence

Building a Custom LLM Chatbot with LangChain, ChromaDB, and LLaMA‑2

This tutorial explains how to leverage generative AI tools—including LLMs, embedding models, vector databases, and the LangChain framework—to create a custom chatbot that answers user queries using a knowledge base, with step‑by‑step code examples for Google Colab.

ChatbotEmbeddingLLM
0 likes · 15 min read
Building a Custom LLM Chatbot with LangChain, ChromaDB, and LLaMA‑2
Kuaishou Tech
Kuaishou Tech
Aug 11, 2023 · Artificial Intelligence

PEPNet: Parameter and Embedding Personalized Network for Multi‑Task Multi‑Domain Recommendation

The paper introduces PEPNet, a plug‑and‑play network that tackles the domain‑seesaw and task‑seesaw problems in multi‑scenario recommendation by using a gated personalization module (GateNU) together with embedding‑level (EPNet) and parameter‑level (PPNet) personalization, and demonstrates its superiority through extensive offline and online experiments on Kuaishou data.

Deep LearningEmbeddinggate network
0 likes · 11 min read
PEPNet: Parameter and Embedding Personalized Network for Multi‑Task Multi‑Domain Recommendation
Baidu Geek Talk
Baidu Geek Talk
Aug 9, 2023 · Industry Insights

Why Vector Retrieval Is the Backbone of Modern LLM Applications

The article explains how vectors represent data in high‑dimensional space, describes the embedding process, outlines the evolution and challenges of vector search, compares exact and approximate algorithms such as IVF, product quantization and HNSW, and details Baidu’s cloud‑native engineering solutions for scalable, filtered vector retrieval.

AICloud NativeEmbedding
0 likes · 14 min read
Why Vector Retrieval Is the Backbone of Modern LLM Applications
Bitu Technology
Bitu Technology
Aug 2, 2023 · Artificial Intelligence

Tubi's Recall Exploration: Embedding‑Based Candidate Generation for Scalable Video Recommendations

This article details Tubi's multi‑stage recommendation system, focusing on the recall phase and describing how popularity metrics, embedding averaging, per‑video nearest‑neighbors, hierarchical clustering, real‑time ranking, and context‑aware sampling are combined to efficiently generate personalized video candidates at scale.

EmbeddingRecommendation SystemsVideo Streaming
0 likes · 10 min read
Tubi's Recall Exploration: Embedding‑Based Candidate Generation for Scalable Video Recommendations
Tencent Cloud Developer
Tencent Cloud Developer
Jul 24, 2023 · Artificial Intelligence

Building an Internal Code Knowledge Base with Embedding and AST Interpreter

The author builds an internal code knowledge base for the TDesign Vue‑Next library by scraping documentation, chunking and embedding texts with OpenAI’s ada model into a vector store, then retrieving relevant chunks for LLM answers, and enhances context continuity using a JavaScript AST interpreter, achieving up to 90 % query accuracy and a 20 % productivity boost.

ASTEmbeddingKnowledge Base
0 likes · 11 min read
Building an Internal Code Knowledge Base with Embedding and AST Interpreter
Nightwalker Tech
Nightwalker Tech
Jul 18, 2023 · Artificial Intelligence

Implementing the Input Processing Layer of a Transformer Model: Tokenization, Embedding, and Positional Encoding

This article explains how to build the input processing stage of a Transformer—including tokenization with Hugging Face tokenizers, token‑to‑embedding conversion using BERT models, custom BPE tokenizers, and positional encoding—providing complete Python code examples and test results.

BPEEmbeddingPositional Encoding
0 likes · 14 min read
Implementing the Input Processing Layer of a Transformer Model: Tokenization, Embedding, and Positional Encoding
ZhongAn Tech Team
ZhongAn Tech Team
Jul 14, 2023 · Artificial Intelligence

Exploring AIGC Applications in Insurance: Insights from ZhongAn Insurance CTO Jiang Jiyun

The interview with ZhongAn Insurance CTO Jiang Jiyun discusses how the company leverages AIGC technologies such as large language models, embeddings, and prompt engineering to enhance marketing, intelligent customer service, and data security, while highlighting practical challenges and best practices for AI adoption in the insurance sector.

AIGCEmbeddingPrompt engineering
0 likes · 15 min read
Exploring AIGC Applications in Insurance: Insights from ZhongAn Insurance CTO Jiang Jiyun
DataFunTalk
DataFunTalk
Jul 13, 2023 · Artificial Intelligence

Time Series Forecasting for NIO Power Swap Stations: Business Background, Challenges, and Algorithm Practice

This article presents NIO's smart energy service platform, focusing on the NIO Power swap‑station business and detailing how time‑series forecasting is applied to predict demand, addressing complex seasonality, holiday drift, growth and competition, and describing the underlying machine‑learning and deep‑learning models and system architecture.

Embeddingenergy servicesmachine learning
0 likes · 16 min read
Time Series Forecasting for NIO Power Swap Stations: Business Background, Challenges, and Algorithm Practice
php Courses
php Courses
Jul 10, 2023 · Backend Development

Embedding HTML in PHP: Common Methods and Code Examples

This article explains several common techniques for embedding HTML within PHP code, including echo statements, mixing PHP inside HTML files, using heredoc syntax, and including external HTML files via the include() function, each illustrated with clear code examples.

BackendEmbeddingHeredoc
0 likes · 3 min read
Embedding HTML in PHP: Common Methods and Code Examples
21CTO
21CTO
Jul 8, 2023 · Artificial Intelligence

Unlocking LangChain: Build End-to-End LLM Apps with Chains, Agents, and Memory

This article introduces LangChain—a modular framework for constructing large‑language‑model applications—covering its core components, asynchronous support, prompt engineering, memory handling, chain and agent workflows, token considerations, embedding techniques, and a step‑by‑step Python example that culminates in a Gradio‑based conversational chatbot.

AI DevelopmentEmbeddingLangChain
0 likes · 20 min read
Unlocking LangChain: Build End-to-End LLM Apps with Chains, Agents, and Memory
Tencent Tech
Tencent Tech
Jul 4, 2023 · Databases

What Is a Vector Database and Why Is It the AI Engine’s Secret Weapon?

This article explains what vectors and vector databases are, how they differ from traditional databases, their core technologies, their relationship with large language models, market trends, and details of Tencent Cloud VectorDB’s capabilities, architecture, real‑world applications, and future competitive challenges.

AIEmbeddingLLM
0 likes · 10 min read
What Is a Vector Database and Why Is It the AI Engine’s Secret Weapon?
IT Services Circle
IT Services Circle
Jun 26, 2023 · Databases

Understanding Vector Databases and Embedding Techniques

The article explains what vector databases are, how vectors and embeddings work, the main embedding methods such as matrix factorization, NLP and graph techniques, the characteristics and high‑availability requirements of vector databases, and common AI‑driven application scenarios like semantic search, recommendation and anomaly detection.

AIEmbeddingmachine learning
0 likes · 8 min read
Understanding Vector Databases and Embedding Techniques
DataFunSummit
DataFunSummit
Jun 21, 2023 · Artificial Intelligence

Graph‑Enhanced Node Representation for Cold‑Start Recommendation: Neighbour‑Enhanced YouTubeDNN

This article proposes a graph‑based node representation method that combines static attribute graphs and dynamic interaction graphs with multi‑level attention to alleviate user and item cold‑start problems in recommendation systems, achieving notable AUC improvements on sparsified MovieLens datasets.

EmbeddingGraph Neural NetworkMovieLens
0 likes · 9 min read
Graph‑Enhanced Node Representation for Cold‑Start Recommendation: Neighbour‑Enhanced YouTubeDNN
ByteDance Web Infra
ByteDance Web Infra
Jun 16, 2023 · Artificial Intelligence

How AIGC Transforms Document Search: Architecture, Techniques, and Future Directions

This article explains how AI‑generated content (AIGC) reshapes document search by combining traditional indexing with modern embedding and prompt‑tuning techniques, reviews key components such as LangChain and Supabase, compares existing AI‑search products, and discusses the future blend of classic and AI‑driven search.

AI searchAIGCEmbedding
0 likes · 15 min read
How AIGC Transforms Document Search: Architecture, Techniques, and Future Directions
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-CUDAComputer VisionDeep Learning
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.

CacheDeep LearningEmbedding
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.

DuktapeEmbeddingJavaScript
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.

Data StandardizationEmbeddingSearch
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.

Deep LearningEmbeddingfraud detection
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 predictionDeep LearningEmbedding
0 likes · 21 min read
Advances in Recommendation Models: CTR Prediction, Continuous Feature Embedding, Interaction Modeling, and Distributed Training
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.

DropoutNetEmbeddingFew‑Shot 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.

EmbeddingMultimodal LearningRecommendation Systems
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.

Deep LearningEmbeddingItem2Vec
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.

EmbeddingKuaishoubias mitigation
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.

DiversityEmbeddingSSD
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.

Backend DevelopmentEmbeddingGo
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.

Embeddingcross-platformmobile-development
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