BestHub
Sign in
Tagged articles

vector retrieval

66 articles · Page 1 of 1
DataFunSummit
DataFunSummit
Aug 10, 2026 · Artificial Intelligence

How Elasticsearch 9.4 Powers Faster, More Accurate, Secure AI‑Agent Search

The article walks through the evolution of enterprise search from keyword‑based retrieval to Agentic AI, detailing Elasticsearch 9.4’s vector‑search optimizations, columnar metric engine, ES|QL pipeline, and Agent infrastructure, and shows benchmark‑backed speed and storage gains that make the platform a solid foundation for AI agents.

AI SearchAgent BuilderBBQ Quantization
0 likes · 15 min read
How Elasticsearch 9.4 Powers Faster, More Accurate, Secure AI‑Agent Search
ByteDance SE Lab
ByteDance SE Lab
Jul 8, 2026 · Databases

Volcano Milvus Hits Nearly 3× VectorDBBench Leader in Retrieval Speed

Under a fixed monthly budget of about 7,100 CNY, Volcano Milvus combines DiskANN and RaBitQ (including Extended‑RaBitQ) with in‑memory layout and query‑path slimming to deliver 20,420 QPS, 2.5 ms average latency, 93.9 % recall, achieving nearly three times the VectorDBBench top score while using roughly one‑third of the memory, disk and compute resources of competing solutions.

Cost EfficiencyDiskANNMilvus
0 likes · 12 min read
Volcano Milvus Hits Nearly 3× VectorDBBench Leader in Retrieval Speed
DataFunSummit
DataFunSummit
Jul 6, 2026 · Artificial Intelligence

From Text to Images: Building Multi‑Modal Product Search with Elasticsearch Serverless

The article explains how modern e‑commerce search is evolving from simple keyword matching to multi‑modal retrieval, outlines a generic architecture that combines text and image embeddings, describes vector similarity metrics and quantization techniques, and demonstrates how Elasticsearch Serverless and Alibaba Cloud AI Search Platform enable a low‑cost, fully managed end‑to‑end multi‑modal product search solution.

ElasticsearchEmbeddingMulti-modal Search
0 likes · 21 min read
From Text to Images: Building Multi‑Modal Product Search with Elasticsearch Serverless
DataFunSummit
DataFunSummit
Jun 25, 2026 · Cloud Computing

From Text to Images: Building Multi‑Modal Product Search with Elasticsearch Serverless

The article walks through the evolution of e‑commerce search from simple keyword matching to multi‑modal retrieval, explains a generic architecture that fuses text and image embeddings, details core techniques such as dense, sparse and hybrid models, vector similarity metrics, quantization methods like SQ and BBQ, and demonstrates how Elasticsearch Serverless provides a server‑less, cost‑effective platform to implement the end‑to‑end solution.

AIElasticsearchEmbedding
0 likes · 21 min read
From Text to Images: Building Multi‑Modal Product Search with Elasticsearch Serverless
DataFunSummit
DataFunSummit
Jun 18, 2026 · Artificial Intelligence

From Text to Images: Building Multimodal Product Search with Elasticsearch Serverless

This article examines the evolution of e‑commerce search from simple keyword matching to multimodal, cross‑modal retrieval, explains the core embedding and vector‑search technologies, compares dense, sparse and hybrid models, and demonstrates how Elasticsearch Serverless and Alibaba Cloud AI Search Platform enable a low‑cost, serverless, high‑performance end‑to‑end multimodal product search solution.

AI search platformBBQElasticsearch Serverless
0 likes · 21 min read
From Text to Images: Building Multimodal Product Search with Elasticsearch Serverless
AI Engineer Programming
AI Engineer Programming
Jun 18, 2026 · Artificial Intelligence

RAG Data Governance: Pre‑Ingestion Data Quality Challenges (Part 1)

The article analyzes how RAG systems inherit classic data‑quality problems, explains why clean input is essential for retrieval and generation, outlines historical GIGO lessons, highlights new risks introduced by vectorization and LLMs, and reviews practical chunking and governance strategies to mitigate hidden failures.

ChunkingData GovernanceLLM
0 likes · 18 min read
RAG Data Governance: Pre‑Ingestion Data Quality Challenges (Part 1)
Java Architect Handbook
Java Architect Handbook
Jun 5, 2026 · Artificial Intelligence

What Is Embedding in RAG and Why Does It Use 1536 Dimensions?

The article explains that embedding converts text into a 1536‑dimensional floating‑point vector that serves as a semantic fingerprint, describes how the vector is generated, why 1536 dimensions are chosen, how similarity is measured, and provides Java Spring AI code examples along with model‑selection guidance and common interview pitfalls.

DimensionEmbeddingOpenAI
0 likes · 16 min read
What Is Embedding in RAG and Why Does It Use 1536 Dimensions?
DataFunSummit
DataFunSummit
May 27, 2026 · Artificial Intelligence

From Text to Images: Building Multi‑Modal Product Search with Elasticsearch Serverless

This article walks through a complete multi‑modal product search solution that transforms textual and visual product data into embeddings, leverages dense, sparse and hybrid models, applies vector similarity and quantization techniques such as SQ and BBQ, and demonstrates how Elasticsearch Serverless provides a serverless, cost‑effective, auto‑scaling backbone for end‑to‑end retrieval.

AI Search Open PlatformElasticsearch ServerlessEmbedding
0 likes · 22 min read
From Text to Images: Building Multi‑Modal Product Search with Elasticsearch Serverless
James' Growth Diary
James' Growth Diary
May 16, 2026 · Artificial Intelligence

Dynamic Tool Selection Unpacked: Let the Agent Choose the Right Tool with Three Strategies

The article analyzes why binding all tools to an LLM agent is costly and error‑prone, presents benchmark data showing token usage dropping six‑fold and error rates falling by up to five times with dynamic selection, and details three practical strategies—vector retrieval, LLM routing, and rule‑semantic hybrid—along with implementation tips, description engineering, multi‑turn handling, and common pitfalls.

AgentLLMLangGraph
0 likes · 17 min read
Dynamic Tool Selection Unpacked: Let the Agent Choose the Right Tool with Three Strategies
DataFunSummit
DataFunSummit
May 15, 2026 · Artificial Intelligence

From Text to Images: Building Multimodal Product Search with Elasticsearch Serverless

The article analyzes the shift from keyword‑based to multimodal e‑commerce search, outlines a generic architecture that combines text and image embedding with vector retrieval, and demonstrates how Elasticsearch Serverless and Alibaba Cloud AI Search platform enable a low‑cost, scalable, and high‑performance product search solution.

AI SearchElasticsearchEmbedding
0 likes · 20 min read
From Text to Images: Building Multimodal Product Search with Elasticsearch Serverless
DeepHub IMBA
DeepHub IMBA
May 11, 2026 · Artificial Intelligence

2026 RAG Selection Guide: How to Choose Between Vector, Graph, and Vectorless

This article compares traditional Vector RAG, GraphRAG, and the newer Vectorless RAG, explains why Vector RAG fails on relational and structured queries, presents benchmark results, outlines each architecture's strengths and costs, and offers a decision framework and Adaptive RAG routing strategy for production systems.

Adaptive RetrievalGraphRAGKnowledge Graph
0 likes · 13 min read
2026 RAG Selection Guide: How to Choose Between Vector, Graph, and Vectorless
DataFunSummit
DataFunSummit
May 7, 2026 · Artificial Intelligence

From Text to Images: Building Multimodal Product Search with Elasticsearch Serverless

This article walks through a complete multimodal product search solution, explaining how embedding and vector retrieval technologies—combined with Elasticsearch Serverless and Alibaba Cloud AI Search—enable image‑based and semantic queries, detailing the architecture, key algorithms, quantization tricks, and practical deployment steps.

AI SearchElasticsearchEmbedding
0 likes · 22 min read
From Text to Images: Building Multimodal Product Search with Elasticsearch Serverless
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Apr 30, 2026 · Artificial Intelligence

Reinventing Search: Alibaba Cloud Elasticsearch Introduces Agent‑Native AI Memory Lake

Facing a projected 175ZB of global data by 2025 and 80% unstructured content, Alibaba Cloud Elasticsearch re‑architects its engine to deliver Agent‑native search, offering structured JSON/Markdown results, high‑performance vector indexing, and a unified enterprise knowledge lake for AI agents.

AI SearchAgentElasticsearch
0 likes · 9 min read
Reinventing Search: Alibaba Cloud Elasticsearch Introduces Agent‑Native AI Memory Lake
James' Growth Diary
James' Growth Diary
Apr 25, 2026 · Artificial Intelligence

Choosing the Right AI Memory: Truncation, Summarization, or Vector Retrieval

This article breaks down LangChain.js's three memory strategies—window truncation, summary compression, and vector‑store retrieval—explaining their inner workings, code setup, trade‑offs in token cost and information retention, and provides a decision guide for selecting the best approach in multi‑turn LLM conversations.

LLMLangChainMemory Management
0 likes · 14 min read
Choosing the Right AI Memory: Truncation, Summarization, or Vector Retrieval
AI Tech Publishing
AI Tech Publishing
Apr 22, 2026 · Artificial Intelligence

Why Longer Context Makes LLMs Forget Faster: 7 Failure Modes and Memory System Solutions

The article analyzes how extending the context window of large language models leads to rapid forgetting, outlines seven concrete failure modes, examines cognitive‑science‑based memory architectures, and walks through practical layers—from Python lists to markdown files to vector retrieval—highlighting why simple context expansion alone cannot solve the problem.

Agent designLLM memorycognitive architecture
0 likes · 10 min read
Why Longer Context Makes LLMs Forget Faster: 7 Failure Modes and Memory System Solutions
DataFunSummit
DataFunSummit
Apr 19, 2026 · Artificial Intelligence

How to Build a Multimodal Product Search Engine with Embedding and Vector Retrieval on Elasticsearch Serverless

This article explains a complete multimodal product search solution that combines text and image embeddings, dense, sparse, and hybrid models, vector similarity metrics, and Elasticsearch Serverless features such as dense_vector, sparse_vector, hybrid search, quantization, and RRF ranking to achieve fast, accurate, and cost‑effective retrieval.

AIElasticsearchEmbedding
0 likes · 20 min read
How to Build a Multimodal Product Search Engine with Embedding and Vector Retrieval on Elasticsearch Serverless
Ma Wei Says
Ma Wei Says
Apr 18, 2026 · Artificial Intelligence

Designing AI Agent Memory: Evolving from Context Stitching to a Self‑Optimizing Cognitive System (Part 2)

The article details a five‑stage framework that upgrades an AI agent's memory from simple token trimming to a dynamic, self‑learning cognitive system, covering vector retrieval, memory lifecycle management, user profiling, explicit/implicit feedback loops, and long‑term governance.

AI AgentHybrid RetrievalMemory Architecture
0 likes · 12 min read
Designing AI Agent Memory: Evolving from Context Stitching to a Self‑Optimizing Cognitive System (Part 2)
DataFunSummit
DataFunSummit
Mar 29, 2026 · Artificial Intelligence

How to Build a Multimodal Product Search Engine with Embedding and Vector Retrieval on Elasticsearch Serverless

This article explores the evolution of e‑commerce search toward multimodal and cross‑modal capabilities, outlines a generic architecture that combines text and image processing via embedding and vector retrieval, and demonstrates how to implement the solution using Alibaba Cloud's AI Search Open Platform and Elasticsearch Serverless with detailed guidance on models, similarity metrics, quantization, and performance optimization.

AIElasticsearchEmbedding
0 likes · 22 min read
How to Build a Multimodal Product Search Engine with Embedding and Vector Retrieval on Elasticsearch Serverless
DataFunSummit
DataFunSummit
Mar 24, 2026 · Artificial Intelligence

How to Build a Multimodal Product Search System with Embedding and Vector Retrieval

This article presents a comprehensive, end‑to‑end solution for multimodal product search, detailing the evolution from keyword to image‑based queries, the core embedding and vector retrieval technologies, practical Elasticsearch Serverless integration, quantization methods, and a complete demo workflow for building a high‑performance, low‑cost search platform.

AI search platformElasticsearchEmbedding
0 likes · 21 min read
How to Build a Multimodal Product Search System with Embedding and Vector Retrieval
Tech Musings
Tech Musings
Feb 10, 2026 · Backend Development

How to Build a Hybrid Vector‑+‑Text Search with Redis 8 (No GPU Required)

This article walks through the complete setup of a hybrid retrieval pipeline on two CPU‑only Linux servers using Redis 8, Qwen‑3‑Embedding vectors, and RediSearch to combine BM25 keyword scores with cosine‑based vector similarity, showing environment details, index creation, data ingestion, the hybrid_search function implementation, result normalization, and a common pitfall of forgetting to set the query language to Chinese.

EmbeddingHybrid SearchPython
0 likes · 23 min read
How to Build a Hybrid Vector‑+‑Text Search with Redis 8 (No GPU Required)
DataFunTalk
DataFunTalk
Nov 11, 2025 · Artificial Intelligence

How Alibaba Cloud’s AI Search Redefines Vector Retrieval and RAG

This article outlines Alibaba Cloud AI Search’s evolution, detailing its dual product lines—enhanced Elasticsearch and self‑developed OpenSearch—key Agentic RAG technologies, serverless architecture, vector and LLM‑driven search capabilities, and future directions in AI‑powered search.

AI SearchAlibaba CloudElasticsearch
0 likes · 4 min read
How Alibaba Cloud’s AI Search Redefines Vector Retrieval and RAG
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Nov 6, 2025 · Artificial Intelligence

How to Optimize RAG Knowledge Base Construction: Parsing, Chunking, and Retrieval

This article explains why building a high‑quality RAG knowledge base is critical, outlines offline parsing techniques for multi‑format documents, presents semantic chunking strategies that preserve structure and context, and shows how to answer interview questions with a robust, production‑ready pipeline.

AI InterviewChunkingKnowledge Base
0 likes · 8 min read
How to Optimize RAG Knowledge Base Construction: Parsing, Chunking, and Retrieval
360 Smart Cloud
360 Smart Cloud
Oct 31, 2025 · Artificial Intelligence

APICLOUD Enterprise Knowledge Base: Architecture, AI Search & Optimization

This article presents a comprehensive solution for constructing an enterprise‑level knowledge base using APICLOUD share‑link data, covering data characteristics, system architecture, core algorithms such as streaming token chunking and semantic vector retrieval, performance optimizations, and real‑world integration scenarios.

APICLOUDEnterprise AIKnowledge Base
0 likes · 16 min read
APICLOUD Enterprise Knowledge Base: Architecture, AI Search & Optimization
AI Large Model Application Practice
AI Large Model Application Practice
Aug 11, 2025 · Artificial Intelligence

How to Build an LLM-Powered Smart Resume Screening System

This article presents a detailed design and implementation of an LLM‑based intelligent resume matching system that combines semantic vector retrieval, structured rule filtering, multi‑dimensional weighted scoring, and natural‑language interaction to create a fast, quantifiable, and explainable hiring pipeline.

AI recruitmentLLMRAG
0 likes · 18 min read
How to Build an LLM-Powered Smart Resume Screening System
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Aug 11, 2025 · Artificial Intelligence

How Multimodal Product Search Transforms E‑Commerce with Embedding and Vector Retrieval

This article explores the evolution from keyword‑based to multimodal e‑commerce search, detailing a universal solution that combines text and image processing through embedding and vector retrieval, and demonstrates how Alibaba Cloud's AI Search Open Platform and Elasticsearch Serverless enable fast, low‑cost, and scalable multimodal product search deployments.

Embeddingmultimodal searchquantization
0 likes · 17 min read
How Multimodal Product Search Transforms E‑Commerce with Embedding and Vector Retrieval
DataFunSummit
DataFunSummit
Jul 16, 2025 · Artificial Intelligence

How Tencent Cloud ES Powers RAG with Hybrid Search and Massive Vector Optimizations

This article explores how Tencent Cloud Elasticsearch combines decades of text search expertise with cutting‑edge vector retrieval and large language models to deliver a one‑stop Retrieval‑Augmented Generation solution, detailing the underlying models, hybrid search architecture, performance tricks, and real‑world case studies.

ElasticsearchHybrid SearchLLM
0 likes · 24 min read
How Tencent Cloud ES Powers RAG with Hybrid Search and Massive Vector Optimizations
DataFunSummit
DataFunSummit
Jun 12, 2025 · Artificial Intelligence

How Alibaba Cloud’s AI Search Evolves with Agentic RAG and Multi‑Model Innovations

This article details Alibaba Cloud AI Search’s development journey, covering its dual product lines, the evolution of Agentic RAG technology, multi‑agent architectures, vector retrieval breakthroughs, GPU‑accelerated indexing, NL2SQL capabilities, deployment models, and future directions for AI‑driven search solutions.

AI SearchGPU AccelerationOpenSearch
0 likes · 33 min read
How Alibaba Cloud’s AI Search Evolves with Agentic RAG and Multi‑Model Innovations
AI Frontier Lectures
AI Frontier Lectures
May 21, 2025 · Artificial Intelligence

New BGE Vector Models Set SOTA in Code and Multimodal Retrieval – What Makes Them So Powerful?

Three newly released BGE vector models—BGE‑Code‑v1, BGE‑VL‑v1.5, and BGE‑VL‑Screenshot—deliver state‑of‑the‑art performance on code, multimodal, and visual document retrieval benchmarks, are open‑source on Hugging Face and GitHub, and aim to boost retrieval‑augmented applications across languages and modalities.

AI modelsBGECode search
0 likes · 8 min read
New BGE Vector Models Set SOTA in Code and Multimodal Retrieval – What Makes Them So Powerful?
AntData
AntData
May 20, 2025 · Artificial Intelligence

How Vector Retrieval Powers AI: Challenges, Solutions, and VSAG’s Open‑Source Breakthrough

The article examines the rapid growth of unstructured data, explains the fundamentals and resource‑intensive nature of vector retrieval, presents Ant Group’s engineering practices—including hybrid HNSW‑DiskANN indexing, performance tricks like BSA pruning and memory prefetching, sparse‑vector and feedback‑driven recall improvements—and outlines the open‑source VSAG roadmap and ecosystem integrations.

AI infrastructureVector Databasehybrid indexing
0 likes · 18 min read
How Vector Retrieval Powers AI: Challenges, Solutions, and VSAG’s Open‑Source Breakthrough
Sohu Tech Products
Sohu Tech Products
Nov 6, 2024 · Artificial Intelligence

RAG2.0 Engine Design Challenges and Implementation

The talk outlines RAG2.0’s design challenges—low vector recall, complex documents, semantic gaps—and presents a two‑stage architecture using deep multimodal understanding and knowledge‑graph‑enhanced retrieval, detailing advanced chunking, multi‑index and multi‑path retrieval, efficient sorting models like ColBERT, and future multi‑modal and memory‑augmented agent directions.

ColBERTDelayed InteractionEnterprise AI
0 likes · 23 min read
RAG2.0 Engine Design Challenges and Implementation
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Oct 22, 2024 · Artificial Intelligence

How Alibaba Cloud Optimizes Enterprise RAG: Key Techniques for AI Search

At the 2024 Alibaba Cloud Yúnxī Conference, senior AI Search expert Xing Shaomin detailed the enterprise‑grade Retrieval‑Augmented Generation (RAG) pipeline, covering critical link architecture, effectiveness, performance, and cost optimizations, as well as practical applications, vector store enhancements, LLM agents, and deployment strategies.

AI SearchEnterprise AILLM
0 likes · 16 min read
How Alibaba Cloud Optimizes Enterprise RAG: Key Techniques for AI Search
Ops Development & AI Practice
Ops Development & AI Practice
Mar 13, 2024 · Artificial Intelligence

How Vector Retrieval Powers AI Model Training and Real-World Applications

Vector retrieval, based on converting data into high‑dimensional vectors and measuring similarity, enables fast, accurate search across massive datasets, supporting AI tasks such as search engines, recommendation, NLP, and computer vision, and plays a crucial role in large‑model training for data selection, anomaly detection, and model optimization.

AI trainingApproximate Nearest NeighborRecommendation Systems
0 likes · 6 min read
How Vector Retrieval Powers AI Model Training and Real-World Applications
Baidu Geek Talk
Baidu Geek Talk
Dec 19, 2023 · Industry Insights

Inside Baidu Search Innovation Contest: Winning AI Solutions Across Five Tracks

The second Baidu Search Innovation Contest attracted over 2,800 participants from 45 regions, featured five AI‑focused tracks, and highlighted champion teams that employed techniques such as Lora‑fine‑tuned LLMs, vector‑intersection Top‑K search, GPU‑optimized algorithms, and diffusion‑based image generation to push the boundaries of search technology.

AI competitionGPU OptimizationLLM fine-tuning
0 likes · 12 min read
Inside Baidu Search Innovation Contest: Winning AI Solutions Across Five Tracks
Xiaohongshu Tech REDtech
Xiaohongshu Tech REDtech
Dec 4, 2023 · Artificial Intelligence

Xiaohongshu Search Engine Innovations Presented at SIGIR-AP 2023

At SIGIR‑AP 2023 in Beijing, Xiaohongshu’s technical team unveiled four key innovations—advanced user‑intent analysis via multi‑stage LLM pre‑training, multimodal vector retrieval, generative inverted‑index enhancements, and a three‑stage relevance‑ranking pipeline with knowledge distillation—to tackle high multi‑intent, long‑tail, and multimodal search challenges for its 260 million‑user platform.

SIGIR-APXiaohongshumultimodal semantic understanding
0 likes · 13 min read
Xiaohongshu Search Engine Innovations Presented at SIGIR-AP 2023
JD Retail Technology
JD Retail Technology
Aug 18, 2023 · Artificial Intelligence

Overview of Recommendation Systems: Definitions, Architecture, Recall, Ranking, and Re‑ranking

This article provides a comprehensive overview of recommendation systems, covering their definition, basic framework, request flow, AB testing, recall strategies (both non‑personalized and personalized), collaborative‑filtering methods, vector‑based retrieval, wide‑and‑deep models, and the MMR re‑ranking algorithm with code examples.

Collaborative FilteringRankingmachine learning
0 likes · 14 min read
Overview of Recommendation Systems: Definitions, Architecture, Recall, Ranking, and Re‑ranking
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.

AIApproximate Nearest NeighborCloud Native
0 likes · 14 min read
Why Vector Retrieval Is the Backbone of Modern LLM Applications
Meituan Technology Team
Meituan Technology Team
Nov 3, 2022 · Artificial Intelligence

Retrieval‑Based Dialogue System for Customer Service at Meituan

This article details Meituan's retrieval‑based dialogue framework for customer service, covering its five‑layer architecture, offline‑to‑online metric system, text and vector recall strategies, ranking models with pre‑training and contrastive learning, and real‑world deployment results across multiple business scenarios.

AICustomer ServiceMeituan
0 likes · 38 min read
Retrieval‑Based Dialogue System for Customer Service at Meituan
Inke Technology
Inke Technology
Oct 27, 2022 · Artificial Intelligence

Scaling Card‑Based Social Matching with Multi‑Task AI Models and Efficient Backend

This article details the design and optimization of Jimu’s card‑based stranger‑social recommendation system, covering product background, gameplay flow, technical challenges in strategy and engineering, a multi‑task AI ranking model, vector recall improvements, and the resulting performance gains.

backend optimizationmulti-task learningrecommendation system
0 likes · 20 min read
Scaling Card‑Based Social Matching with Multi‑Task AI Models and Efficient Backend
Architects Research Society
Architects Research Society
Jun 6, 2022 · Artificial Intelligence

Neural Search in Apache Solr: Dense Vector Fields, HNSW Graphs, and K‑Nearest Neighbor Implementation

This article explains how Apache Solr and Lucene implement neural search using dense vector fields, hierarchical navigable small‑world (HNSW) graphs, and approximate K‑nearest neighbor algorithms, covering configuration, custom codecs, indexing formats, and query parsers for vector‑based retrieval.

Apache SolrDense VectorsHNSW
0 likes · 15 min read
Neural Search in Apache Solr: Dense Vector Fields, HNSW Graphs, and K‑Nearest Neighbor Implementation
DaTaobao Tech
DaTaobao Tech
May 27, 2022 · Artificial Intelligence

Multimodal Pretraining for Search Recall in E-commerce

The paper proposes a multimodal pre‑training framework that jointly encodes query text and item titles with images via shared and single‑stream towers, using MLM, MPM, QIC, and matching tasks, and demonstrates substantial Recall@K gains on a billion‑item e‑commerce catalog by leveraging visual cues to bridge the semantic gap.

Multimodale-commercepretraining
0 likes · 17 min read
Multimodal Pretraining for Search Recall in E-commerce
Hulu Beijing
Hulu Beijing
May 26, 2022 · Artificial Intelligence

Why Vector Retrieval Outperforms Keyword Search for Personalized Video Discovery

This article explains how modern video platforms combine traditional keyword retrieval with deep‑learning‑based vector retrieval, detailing model architectures, attention mechanisms, personalization features, offline experiments, and online A/B results that show significant improvements in recall, relevance, and user experience.

deep learninginformation retrievalkeyword-search
0 likes · 18 min read
Why Vector Retrieval Outperforms Keyword Search for Personalized Video Discovery
Shopee Tech Team
Shopee Tech Team
Feb 17, 2022 · Artificial Intelligence

From Zero to One: Building and Optimizing Dropdown Recommendation in Shopee Chatbot

The article details Shopee Chatbot’s end‑to‑end development of a dropdown recommendation feature, describing the retrieve‑then‑rank architecture with BM25 and vector recalls, multilingual pre‑training and distillation, DeepFM‑based ranking, experimental gains in CTR and conversion, deployment infrastructure, business impact, and future enhancements.

CTR predictionChatbotRanking
0 likes · 20 min read
From Zero to One: Building and Optimizing Dropdown Recommendation in Shopee Chatbot
Xianyu Technology
Xianyu Technology
Jan 29, 2022 · Artificial Intelligence

Semantic Vector Retrieval and I2I Recall Optimization in Xianyu Search

Xianyu search recall stage upgraded from simple text matching to semantic vector retrieval using DSSM with Electra‑Small, query graph attention, and behavior‑based I2I, adding structured attributes and OCR tags, improving AUC to 0.824 and HitRate@10 to 90.1%, boosting purchase metrics by up to 4%.

Xianyudeep learningrecommendation
0 likes · 17 min read
Semantic Vector Retrieval and I2I Recall Optimization in Xianyu Search
IEG Growth Platform Technology Team
IEG Growth Platform Technology Team
Jan 17, 2022 · Artificial Intelligence

Introduction to Vector Retrieval, Distance Metrics, and Fundamental Algorithms

This article introduces the concept of vector retrieval, outlines its diverse application scenarios, explains common distance metrics for both floating‑point and binary vectors, and surveys fundamental approximate nearest‑neighbor algorithms including tree‑based, graph‑based, quantization, and hashing methods.

Approximate Nearest NeighborHNSWKD-Tree
0 likes · 22 min read
Introduction to Vector Retrieval, Distance Metrics, and Fundamental Algorithms
Laiye Technology Team
Laiye Technology Team
Jan 7, 2022 · Artificial Intelligence

Understanding Vector Retrieval: Principles, Applications, and High‑Performance Algorithms

This article explains how deep learning transforms unstructured data into dense vectors, defines vector retrieval, outlines its many use cases such as product, video, and text search, discusses challenges in learning effective embeddings, and reviews high‑performance algorithms like LSH, neighbor graphs, and product quantization.

AI ApplicationsApproximate Nearest NeighborHNSW
0 likes · 21 min read
Understanding Vector Retrieval: Principles, Applications, and High‑Performance Algorithms
Kuaishou Tech
Kuaishou Tech
Dec 10, 2021 · Artificial Intelligence

Kuaishou and Tsinghua University Win NeurIPS'21 Billion-Scale ANN Challenge with FAISS‑Optimized KST_ANN Solution

On December 6, Kuaishou and Tsinghua University’s joint team secured first place in the NeurIPS'21 Billion‑Scale Approximate Nearest Neighbor Search Challenge by leveraging a FAISS‑optimized, memory‑efficient KST_ANN algorithm that achieved over 6% higher recall on multiple billion‑scale datasets, showcasing the practical impact of large‑scale vector retrieval in AI‑driven services.

AIANNFAISS
0 likes · 5 min read
Kuaishou and Tsinghua University Win NeurIPS'21 Billion-Scale ANN Challenge with FAISS‑Optimized KST_ANN Solution
58 Tech
58 Tech
Nov 18, 2021 · Artificial Intelligence

Intelligent Search Strategy for 58 Recruitment: Breaking Category Constraints and Building a Smart Recall Framework

This article describes how 58 recruitment revamped its search system by removing rigid category limits, introducing query rewriting, intent recognition, doc understanding, and vector‑based recall, resulting in significantly higher relevance, reduced bad cases, and improved commercial performance.

AIQuery RewritingRecruitment
0 likes · 14 min read
Intelligent Search Strategy for 58 Recruitment: Breaking Category Constraints and Building a Smart Recall Framework
DataFunSummit
DataFunSummit
Dec 6, 2020 · Artificial Intelligence

E‑commerce Search Engine Recall and Vector Retrieval Techniques

This article explains how e‑commerce platforms use inverted indexes for fast word‑based recall, introduce vector‑based semantic retrieval, and combine deep‑learning models such as DSSM and DeepMatch with real‑time user behavior attention networks to generate efficient, personalized candidate sets for ranking.

recallvector retrieval
0 likes · 15 min read
E‑commerce Search Engine Recall and Vector Retrieval Techniques
iQIYI Technical Product Team
iQIYI Technical Product Team
Jun 19, 2020 · Artificial Intelligence

Emoji Search at iQIYI Douya: From ElasticSearch to Lucene and Semantic Retrieval

iQIYI Douya’s emoji search evolved from ElasticSearch to a pure Lucene implementation and added semantic vector retrieval, enabling fast, scalable, and more accurate text‑based search of AI‑generated images for small‑to‑medium businesses by combining custom tokenization, dense embeddings, and hybrid ranking.

ElasticsearchLuceneSearch Architecture
0 likes · 14 min read
Emoji Search at iQIYI Douya: From ElasticSearch to Lucene and Semantic Retrieval
DataFunTalk
DataFunTalk
Jun 17, 2020 · Artificial Intelligence

Deep Recall and Vector Retrieval in 58 Recruitment Recommendation System

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

AIcost-sensitive lossdeep learning
0 likes · 15 min read
Deep Recall and Vector Retrieval in 58 Recruitment Recommendation System
Youku Technology
Youku Technology
Jun 17, 2020 · Industry Insights

How Youku’s Multi‑Modal Search Engine Powers Billion‑Scale Video Retrieval

This article details the design and implementation of Youku’s Multi‑Modal Search Engine (MMS), covering its distributed multi‑level indexing architecture, vector retrieval using Aitheta, cross‑modal query scheduling, graph‑based execution engine, and real‑world applications such as intelligent video search and image‑based series lookup.

Video platformgraph execution enginelarge-scale indexing
0 likes · 10 min read
How Youku’s Multi‑Modal Search Engine Powers Billion‑Scale Video Retrieval
DataFunTalk
DataFunTalk
Jun 3, 2020 · Artificial Intelligence

Semantic Retrieval and Product Ranking in JD E‑commerce Search

This article presents JD's e‑commerce search system, detailing the semantic vector retrieval and product ranking pipelines, the two‑tower deep learning architecture, attention‑based personalization, negative sampling strategies, training optimizations, and real‑world performance gains achieved in production.

Rankingdeep learninge-commerce
0 likes · 11 min read
Semantic Retrieval and Product Ranking in JD E‑commerce Search
DataFunTalk
DataFunTalk
Apr 6, 2020 · Artificial Intelligence

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

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

ANNPythonRecommendation Systems
0 likes · 10 min read
Introducing DeepMatch: An Open‑Source Library for Deep Retrieval Matching Algorithms
Meituan Technology Team
Meituan Technology Team
Oct 10, 2019 · Artificial Intelligence

Iterative Development of Delivery Time Estimation Models: Tree Model, Vector Retrieval, and End‑to‑End Deep Learning

The paper chronicles Meituan’s three‑stage evolution of delivery‑time estimation—from a hierarchical address tree with local linear regression, through a vector‑retrieval system that boosts recall, to a lightweight end‑to‑end deep‑learning model that meets sub‑5 ms latency while delivering progressively lower error and full coverage.

deep learningdelivery time estimationlogistics
0 likes · 21 min read
Iterative Development of Delivery Time Estimation Models: Tree Model, Vector Retrieval, and End‑to‑End Deep Learning
Xianyu Technology
Xianyu Technology
Sep 7, 2018 · Artificial Intelligence

Video Deduplication on Xianyu Using High‑Dimensional Vector Retrieval

The Xianyu platform combats video plagiarism by extracting key frames, converting them into 1024‑dimensional vectors, and using product quantization‑based high‑dimensional vector retrieval to achieve over 95% recall with ~100 ms latency and more than 1000 QPS, enabling scalable video, image, and product deduplication.

FAISSPQhigh-dimensional vectors
0 likes · 12 min read
Video Deduplication on Xianyu Using High‑Dimensional Vector Retrieval