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vector-search

219 articles · Page 3 of 3
IEG Growth Platform Technology Team
IEG Growth Platform Technology Team
Mar 21, 2022 · Backend Development

Optimization of Local Vector Retrieval: Filtering, Storage, and Sorting Strategies

This article presents a comprehensive study of local vector retrieval optimization, covering memory‑based filtering techniques, Redis‑backed vector storage designs, and various sorting algorithms—including radix and heap‑based approaches—to achieve lower latency and higher throughput for large‑scale ad recommendation systems.

FilteringRedisbackend optimization
0 likes · 13 min read
Optimization of Local Vector Retrieval: Filtering, Storage, and Sorting Strategies
DataFunTalk
DataFunTalk
Mar 2, 2022 · Artificial Intelligence

Huya Live Streaming Recommendation Architecture: Business Background, System Design, Vector Retrieval, and Ranking

This article presents a comprehensive overview of Huya Live's recommendation system, covering business background, system architecture, vector retrieval techniques, ranking pipeline, technical challenges, implementation details, and future outlook, highlighting scalability and performance optimizations.

AIHuyaRanking
0 likes · 14 min read
Huya Live Streaming Recommendation Architecture: Business Background, System Design, Vector Retrieval, and Ranking
Baidu Geek Talk
Baidu Geek Talk
Feb 14, 2022 · Artificial Intelligence

How Baidu’s PUCK Dominated the First BigANN Vector Search Competition

The inaugural BigANN competition, organized by NeurIPS, showcased large‑scale ANN research, and Baidu's self‑developed PUCK algorithm secured top scores across all four tracks by leveraging multi‑layer quantization, two‑level inverted indexing, and extensive system‑level optimizations.

ANNApproximate Nearest NeighborBigANN
0 likes · 8 min read
How Baidu’s PUCK Dominated the First BigANN Vector Search Competition
Code DAO
Code DAO
Dec 26, 2021 · Artificial Intelligence

Building a Vector‑Based Movie Recommendation System with Transformers

This tutorial walks through constructing a movie recommendation engine by downloading a dataset, cleaning and de‑duplicating entries, encoding plot summaries into vectors with transformer models, and performing nearest‑neighbor searches using scikit‑learn, while handling misspellings with Levenshtein distance.

Levenshtein distancePandasTransformers
0 likes · 8 min read
Building a Vector‑Based Movie Recommendation System with Transformers
Laravel Tech Community
Laravel Tech Community
Dec 9, 2021 · Backend Development

Apache Lucene 9.0 Released – New Features and Improvements

Apache Lucene 9.0, a high‑performance Java full‑text search library, introduces high‑dimensional vector indexing, new language analyzers, faster faceting and sorting, updated file formats, and several performance optimizations, providing developers with a richer, more efficient search toolkit.

Apache LuceneFull-text Searchjava
0 likes · 3 min read
Apache Lucene 9.0 Released – New Features and Improvements
Kuaishou Tech
Kuaishou Tech
Nov 29, 2021 · Artificial Intelligence

Starry Vector Retrieval Platform: Architecture, Features, and Performance

The article describes the design, challenges, architecture, key features, algorithm optimizations, and future roadmap of Kuaishou's Starry vector retrieval platform, which delivers high‑performance, high‑reliability, and easy‑to‑use large‑scale ANN search for diverse business scenarios.

AI platformANNDistributed Architecture
0 likes · 14 min read
Starry Vector Retrieval Platform: Architecture, Features, and Performance
iQIYI Technical Product Team
iQIYI Technical Product Team
Aug 20, 2021 · Artificial Intelligence

Engineering Practice of Online Vector Recall Service at iQIYI

iQIYI’s engineering team built an online vector‑recall service on Milvus, wrapping it with a Dubbo‑gRPC interface to serve 6 M 64‑dimensional embeddings at roughly 3 k QPS and 20 ms p99 latency, integrating query‑embedding generation, simplifying recommendation pipelines, and demonstrating the performance and operational advantages of a platformized ANN‑based recall layer.

AIEngineeringMilvus
0 likes · 14 min read
Engineering Practice of Online Vector Recall Service at iQIYI
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 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
Baidu Geek Talk
Baidu Geek Talk
May 10, 2021 · Industry Insights

How Baidu’s GNOIMI Powers Billion‑Scale Rich Media Retrieval

Baidu’s rich‑media retrieval system combines CNN‑based feature extraction with an Approximate Nearest Neighbor engine called GNOIMI, employing hierarchical clustering, product quantization, and optimized indexing to achieve sub‑millisecond search over billions of images, videos and audio, supporting anti‑spam, recommendation and risk‑control across dozens of services.

ANNGNOIMIHNSW
0 likes · 16 min read
How Baidu’s GNOIMI Powers Billion‑Scale Rich Media Retrieval
Alibaba Cloud Developer
Alibaba Cloud Developer
Mar 4, 2021 · Artificial Intelligence

How Alibaba’s Proxima Engine Revolutionizes Vector Search for AI Applications

Alibaba’s Damo Academy unveils Proxima, a high‑performance vector search engine that powers e‑commerce, video, and payment services, detailing its core capabilities, large‑scale indexing, distributed construction, real‑time updates, and challenges such as algorithm diversity, scalability, and multi‑modal retrieval.

AIAlibaba Proximalarge-scale indexing
0 likes · 17 min read
How Alibaba’s Proxima Engine Revolutionizes Vector Search for AI Applications
58 Tech
58 Tech
Mar 3, 2021 · Artificial Intelligence

Design and Implementation of a Faiss‑Based Vector Search Platform

The article describes the design, architecture, and key components of a vector search platform built on Faiss that supports full‑index construction, incremental and distributed indexing, online retrieval, city‑level search, and vector update/delete operations to meet large‑scale AI application needs.

AIKubernetesLarge-Scale Retrieval
0 likes · 10 min read
Design and Implementation of a Faiss‑Based Vector Search Platform
System Architect Go
System Architect Go
Jun 4, 2020 · Artificial Intelligence

Evolution and Underlying Principles of the Billion‑Scale Image Search System at Youpai Image Manager

This article describes the two‑generation evolution of Youpai Image Manager's billion‑scale image search system, explaining the mathematical representation of images, the limitations of MD5, the first‑generation pHash‑ElasticSearch solution, and the second‑generation CNN‑Milvus approach for robust, large‑scale visual similarity search.

CNNMilvusimage search
0 likes · 9 min read
Evolution and Underlying Principles of the Billion‑Scale Image Search System at Youpai Image Manager
System Architect Go
System Architect Go
Mar 30, 2020 · Artificial Intelligence

Overview of Image Search System

This article explains the fundamentals of building an image‑by‑image search system, covering image feature extraction methods such as hashing, traditional descriptors, CNN‑based vectors, and the use of vector search engines like Milvus for similarity retrieval.

CNNMilvusfeature extraction
0 likes · 6 min read
Overview of Image Search System
DataFunTalk
DataFunTalk
Oct 24, 2019 · Artificial Intelligence

Evolution and Engineering Practices of the 360 Display Advertising Recall System

This article details the 360 display advertising system's architecture and the progressive evolution of its recall module, covering business overview, overall pipeline, various recall strategies—including Boolean, vectorized, and deep‑tree approaches—and the performance optimizations applied to meet real‑time constraints.

Advertisingdeep learningrecall system
0 likes · 14 min read
Evolution and Engineering Practices of the 360 Display Advertising Recall System
360 Quality & Efficiency
360 Quality & Efficiency
Aug 23, 2019 · Artificial Intelligence

High‑Performance High‑Dimensional Vector KNN Search Using FAISS

This article introduces the background of vector representations in machine learning, explains the K‑Nearest Neighbors algorithm and its key parameters, reviews traditional tree‑based and modern high‑performance search solutions, and demonstrates how FAISS can achieve microsecond‑level KNN queries on large‑scale high‑dimensional data.

FAISSKNNhigh-dimensional
0 likes · 5 min read
High‑Performance High‑Dimensional Vector KNN Search Using FAISS
vivo Internet Technology
vivo Internet Technology
Nov 16, 2018 · Artificial Intelligence

Efficient Vector Search with Deep Learning Embeddings in Elasticsearch

The article explains how to replace keyword matching with deep‑learning document embeddings in Elasticsearch by applying PCA dimensionality reduction, indexing vectors using Lucene’s KD‑tree structures via a custom plugin, and leveraging FAISS‑style nearest‑neighbour techniques to achieve fast, semantically aware similarity search.

ElasticsearchFAISSKD-Tree
0 likes · 7 min read
Efficient Vector Search with Deep Learning Embeddings in Elasticsearch
Xianyu Technology
Xianyu Technology
Aug 31, 2018 · Artificial Intelligence

Personalized Recommendation for Xianyu Small Item Pools: Challenges and Solutions

Xianyu’s personalized recommendation system struggles with tiny, fast‑turnover item pools because traditional X2I matrices provide insufficient recall, so the team introduced pool‑specific pre‑filtering, high‑dimensional vector search, and a real‑time search‑engine recall, the latter boosting clicks by 14 % and transactions by 0.14 %.

EngineeringXianyupersonalization
0 likes · 15 min read
Personalized Recommendation for Xianyu Small Item Pools: Challenges and Solutions