How Agentic Architectures Power the Next‑Gen Recommendation and Search Systems

This article summarizes a technical ebook that analyzes the evolution of recommendation and search systems—from deep‑learning models to large‑language‑model agents—detailing multi‑agent RAG architectures, Huawei’s KAR knowledge adapters, Baidu’s generative ranking (GRAB), Elasticsearch vector search, and performance results such as a 1.5% AUC lift and GPU‑accelerated throughput gains.

ElasticsearchGenerative RankingMulti-Agent Architecture

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How Agentic Architectures Power the Next‑Gen Recommendation and Search Systems

DataFunSummit

Dec 12, 2024 · Artificial Intelligence

Exploring Generative Retrieval: Memory Mechanisms, GDR Paradigm, and Practical Applications

This presentation examines generative retrieval (GDR), compares it with sparse and dense retrieval paradigms, analyzes memory‑mechanism challenges from an EACL 2024 paper, reports experimental findings, proposes a hybrid GDR‑dense approach, and outlines real‑world application scenarios and future directions.

GDRGenerative RetrievalMemory Mechanism

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Exploring Generative Retrieval: Memory Mechanisms, GDR Paradigm, and Practical Applications

政采云技术

Jan 4, 2023 · Artificial Intelligence

Overview of Recommendation and Search System Architecture: Recall and Ranking Techniques

This article explains the architecture of recommendation and search systems, detailing various recall methods such as collaborative filtering, matrix factorization, and vector‑based approaches, as well as ranking models like LR, FM, and DeepFM, and discusses re‑ranking and traffic control strategies.

artificial intelligencerankingrecall

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