This article examines practical ways to improve Retrieval‑Augmented Generation (RAG) retrieval quality—covering vector database choices, data chunking, embedding models, query expansion, and re‑ranking—while weighing performance gains against operational costs through multiple real‑world case studies.
This guide explains why InnoDB fuzzy queries lose indexes, introduces MySQL full‑text search with inverted indexes, shows how to create and use full‑text indexes via CREATE TABLE and ALTER statements, and demonstrates natural language, boolean, and query‑expansion modes with practical SQL examples.
This guide explains how MySQL InnoDB full‑text search works, covering inverted indexes, index creation, query syntax for natural language, boolean, and query‑expansion modes, relevance calculation, and index maintenance.
This article explains MySQL's InnoDB full‑text search, covering the theory of inverted indexes, how to create and use full‑text indexes with various MATCH…AGAINST modes, Boolean operators, query expansion, relevance calculation, and index removal, illustrated with practical SQL examples and diagrams.
This article showcases three JD tech case studies—using large language models for e‑commerce query expansion, applying sparse large models with scaling‑law experiments to improve ad prediction, and building proactive risk‑prevention systems—to illustrate practical AI engineering that drives higher recall, conversion, and system robustness.
Young JD algorithm engineers share how they solve tough AI problems—from optimizing large‑model training and reward‑model design for ad image generation, to building LLM‑based query expansion, agent evaluation, and model pruning with FFT and RDP—illustrating practical breakthroughs and personal growth in cutting‑edge AI research.
This article details how JD Retail's young algorithm engineers tackled a series of AI engineering problems—including advertising image quality assessment with multi‑reward models, large‑language‑model‑driven query expansion, FFT‑and‑RDP‑based model pruning, and agent‑centric reinforcement learning—while sharing practical growth insights and code snippets.
JD Retail’s engineering team tackles hard AI problems by replacing a monolithic reward model with specialized small models for ad‑image generation, deploying an LLM‑driven query‑expansion pipeline that lifts conversion rates, and pruning text‑to‑image transformers using FFT and RDP to boost throughput 40% without loss, while building comprehensive evaluation tools and a semantic smart‑assistant.
This article explains how MySQL InnoDB implements full‑text search using inverted indexes, shows how to create and drop full‑text indexes, and demonstrates the three query modes—Natural Language, Boolean, and Query Expansion—along with their syntax, operators, relevance calculation, and practical examples.
This article explains how MySQL InnoDB implements full‑text search, covering the underlying inverted index structures, how to create and drop full‑text indexes, the MATCH…AGAINST syntax, and detailed examples of natural language, boolean, and query‑expansion search modes with practical SQL demos.
This article explains how MySQL InnoDB implements full‑text search using inverted indexes, covers the creation and usage of FULLTEXT indexes, demonstrates various MATCH…AGAINST query modes such as natural language, boolean, and query expansion, and shows how to manage and delete full‑text indexes with practical SQL examples.
This article explains how MySQL InnoDB implements full‑text search using inverted indexes, shows how to create and drop FULLTEXT indexes, and demonstrates natural language, boolean, and query‑expansion search modes with concrete SQL examples and operator details.
This article explains why traditional B‑Tree indexes fail for keyword searches, introduces the concept of inverted indexes, shows how to create and use MySQL InnoDB full‑text indexes with MATCH‑AGAINST in various modes, and covers index maintenance and query‑expansion techniques.
This article explains how InnoDB implements full-text search using inverted indexes, shows how to create and use full-text indexes with various query modes—including natural language, boolean, and query expansion—covers relevance calculation, stopwords, token size limits, and demonstrates how to delete indexes, all illustrated with SQL examples and diagrams.
This tutorial explains how MySQL implements full‑text search, covering the creation of full‑text indexes (including Chinese ngram support), the three query modes (natural language, boolean, and query expansion), relevance ranking, underlying inverted‑index structures, cache handling, and common DML operations.
This article explains MySQL full‑text indexing, compares its syntax with ordinary SQL, demonstrates how to create and query a full‑text index using natural language, boolean, and query‑expansion modes, and shows performance differences through execution‑plan analysis and relevance ranking.
This article reviews current query expansion methods, contrasting relevance‑based models that rely on terms or entities with generative models that encode whole queries, discusses challenges of handling long and complex queries, and surveys recent research on encoding queries, session modeling, and multi‑task feature integration.
This article reviews the factors influencing search results, explains why query expansion is crucial for improving recall, surveys various sources of expansion terms, describes probabilistic and translation‑based models, and offers practical recommendations for building effective, data‑driven query expansion pipelines.
The article introduces the second edition of "Computational Advertising", highlights its practical coverage of bidding algorithms, eCPM estimation, query expansion methods, and ad placement optimization, while also noting its industry impact, author credentials, and a limited‑time book giveaway.
This article explains how modern search engines identify precise and broad user intents, examines real‑world query examples, and details extension modules such as synonym, pinyin, and correction that enhance query understanding using algorithms like Aho‑Corasick, Hidden Markov Models, and Levenshtein distance.
