Vector Retrieval

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 AccelerationLarge Models

0 likes · 33 min read

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

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 InteractionDocument Understanding

0 likes · 23 min read

RAG2.0 Engine Design Challenges and Implementation

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.

Artificial IntelligenceSIGIR-APSearch Engine

0 likes · 13 min read

Xiaohongshu Search Engine Innovations Presented at SIGIR-AP 2023

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.

RankingRecommendation systemsVector Retrieval

0 likes · 14 min read

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

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.

Vector Retrievalbackend optimizationmulti-task learning

0 likes · 20 min read

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

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

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.

PretrainingVector Retrievaldeep learning

0 likes · 17 min read

Multimodal Pretraining for Search Recall in E-commerce

DataFunSummit

Mar 31, 2022 · Artificial Intelligence

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

This article presents a comprehensive overview of Huya's live‑streaming recommendation system, covering business background, overall architecture, vector‑based retrieval, detailed ranking pipeline, technical challenges, deployment strategies, scalability, and future outlook.

AILive StreamingRanking

0 likes · 18 min read

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

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

DataFunSummit

Feb 12, 2022 · Artificial Intelligence

Advances and Challenges in Post‑BERT Semantic Matching: Negative Sampling, Data Augmentation, and Applications

After the BERT era, this article reviews the limitations of pre‑trained language models for semantic matching, discusses negative‑sample sampling, data‑augmentation techniques, contrastive learning methods such as ConSERT and SimCSE, and practical deployment considerations in vector‑based retrieval systems.

Vector Retrievalcontrastive learningdata augmentation

0 likes · 20 min read

Advances and Challenges in Post‑BERT Semantic Matching: Negative Sampling, Data Augmentation, and Applications

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%.

Semantic SearchVector RetrievalXianyu

0 likes · 17 min read

Semantic Vector Retrieval and I2I Recall Optimization in Xianyu Search

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.

HNSWKD-TreeLSH

0 likes · 22 min read

Introduction to Vector Retrieval, Distance Metrics, and Fundamental Algorithms

DataFunSummit

Jan 10, 2022 · Artificial Intelligence

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

This article explains how deep learning transforms raw physical‑world data into dense vectors, defines the significance of vector retrieval, surveys common use cases such as image, video, and text search, discusses challenges in representation learning, and reviews high‑performance approximate nearest‑neighbor algorithms and practical deployments.

AI applicationsVector Retrievalapproximate nearest neighbor

0 likes · 21 min read

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

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 applicationsHNSWLSH

0 likes · 21 min read

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.

AIANNKST_ANN

0 likes · 5 min read

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

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.

AIRecruitmentVector Retrieval

0 likes · 14 min read

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

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.

Search EngineVector Retrievaldeep learning

0 likes · 15 min read

E‑commerce Search Engine Recall and Vector Retrieval Techniques

58 Tech

Dec 2, 2020 · Artificial Intelligence

AI Engineering Architecture Salon: Backend Design for Intelligent Customer Service, Speech Recognition Engine, Lingxi Voice Analysis Platform, and High‑Performance Vector Retrieval

The online AI Engineering Architecture Salon, organized by 58.com AI Lab, will present four technical sessions on December 3 and 8 covering backend design for intelligent customer service, speech recognition engine, the Lingxi voice analysis platform, and a high‑performance vector retrieval system, each with detailed abstracts and expert speakers.

58.comAIOnline Seminar

0 likes · 8 min read

AI Engineering Architecture Salon: Backend Design for Intelligent Customer Service, Speech Recognition Engine, Lingxi Voice Analysis Platform, and High‑Performance Vector Retrieval

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.

ElasticsearchImage SearchLucene

0 likes · 14 min read

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

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.

AIVector Retrievalcost-sensitive loss

0 likes · 15 min read

Deep Recall and Vector Retrieval in 58 Recruitment Recommendation System