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Mingyi World Elasticsearch
Mingyi World Elasticsearch
Mar 11, 2025 · Backend Development

Master Elasticsearch dense_vector: definition, usage, and kNN search guide

This article explains Elasticsearch's dense_vector field for storing dense vectors, covering its definition, how to define and index vectors, kNN search methods (brute‑force and approximate with HNSW), similarity options, quantization strategies, bit‑vector support, key parameters, and how to update mappings.

Elasticsearchbit vectorsdense_vector
0 likes · 13 min read
Master Elasticsearch dense_vector: definition, usage, and kNN search guide
Model Perspective
Model Perspective
Aug 24, 2024 · Fundamentals

Why Vectors Are the Secret Sauce Behind Modern AI and Everyday Tech

Vectors, mathematical objects capturing magnitude and direction, serve as a versatile tool for representing multidimensional data, enabling everything from economic indicators and navigation cues to deep-learning feature extraction, similarity measures, and applications like music recognition, smart chatbots, and image search.

data representationmachine learningsimilarity
0 likes · 9 min read
Why Vectors Are the Secret Sauce Behind Modern AI and Everyday Tech
Model Perspective
Model Perspective
Jun 4, 2022 · Fundamentals

Understanding Sample Similarity: Distance Metrics and Cluster Methods

This article explains how to quantify similarity between data samples using distance metrics such as Manhattan, Euclidean, and Chebyshev, outlines the properties these distances must satisfy, and describes common inter‑class measures like single linkage, complete linkage, centroid, group average, and sum‑of‑squares methods.

Minkowskiclusteringdistance metrics
0 likes · 4 min read
Understanding Sample Similarity: Distance Metrics and Cluster Methods
DataFunTalk
DataFunTalk
Sep 18, 2021 · Artificial Intelligence

Unsupervised Algorithms for Fraud Detection in Huya's Risk Control System

This article presents Huya's exploration of unsupervised learning techniques for risk control, detailing business risk scenarios, black‑market attack vectors, limitations of traditional defenses, and the design, implementation, and evaluation of graph‑based and density‑based clustering methods to automatically discover and mitigate fraudulent user groups.

AIHuyaUnsupervised Learning
0 likes · 11 min read
Unsupervised Algorithms for Fraud Detection in Huya's Risk Control System
JD Tech
JD Tech
Feb 12, 2019 · Artificial Intelligence

Content‑Based Filtering: Concepts, Implementation, and Pros/Cons

The article explains content‑based filtering for recommendation systems, covering its basic concepts, feature requirements, implementation using vector representations and cosine similarity, advantages and disadvantages, and supplementary algorithms such as k‑Nearest Neighbor, Rocchio, decision trees, linear classifiers, and Naive Bayes.

Naive BayesRocchiocontent-based filtering
0 likes · 11 min read
Content‑Based Filtering: Concepts, Implementation, and Pros/Cons
Architect
Architect
Feb 26, 2016 · Artificial Intelligence

User-Based Collaborative Filtering Recommendation Algorithm Explained

This article introduces the concept and history of recommendation algorithms, outlines the basic conditions for recommendations, and provides a detailed explanation of user-based collaborative filtering, including similarity calculations, neighbor selection, recommendation scoring, practical code snippets, and discussion of potential issues.

algorithmcollaborative filteringrecommendation
0 likes · 12 min read
User-Based Collaborative Filtering Recommendation Algorithm Explained