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🎯 Retrieval Systems

Multi-Stage Ranking, Candidate Selection, Re-ranking Models, Precision Metrics

OneSearch: A Preliminary Exploration of the Unified End-to-End Generative Framework for E-commerce Search
arxiv.org·4h
👤Search Personalization
Fantastic Pretraining Optimizers and Where to Find Them
arxiviq.substack.com·21h·
Discuss: Substack
📊Quantization
Quirks of Spotlight local search
eclecticlight.co·2h
🔍Search Indexing
Exploring the Design Space of Fair Tree Learning Algorithms
arxiv.org·4h
🧠Learned Indexing
Hierarchical Multi-Interest Co-Network For Coarse-Grained Ranking
arxiv.org·4h
⚖️Feed Ranking
Automated Prior Art Landscape Analysis via Semantic Graph Traversal and HyperScore Prioritization
dev.to·10h·
Discuss: DEV
🧭Content Discovery
HF-RAG: Hierarchical Fusion-based RAG with Multiple Sources and Rankers
arxiv.org·4h
📊Multi-vector RAG
Agents need a new retrieval engine
hornet.dev·2d·
Discuss: Hacker News
🌐Federated Search
Diverse Unionable Tuple Search: Novelty-Driven Discovery in Data Lakes [Technical Report]
arxiv.org·1d
🗂️Vector Databases
STRive: An association rule-based system for the exploration of spatiotemporal categorical data
arxiv.org·4h
🕵️Metadata Mining
REFRAG: Rethinking RAG based Decoding
arxiv.org·1d
📄Text Chunking
Efficient Dynamic Rank Aggregation
arxiv.org·4h
🧠Query Planners
The Nah Bandit: Modeling User Non-compliance in Recommendation Systems
arxiv.org·4h
🎯Recommendation Metrics
Learning Mechanism Underlying NLP Pre-Training and Fine-Tuning
arxiv.org·4h
🤖Grammar Induction
How Dataset Imbalances Shape Medical Image Retrieval Accuracy
hackernoon.com·6d
🔍Semantic Search
All Hype, No Bite: The "Your Brain on ChatGPT.." Preprint
residualinsights.com·14h·
Discuss: Hacker News
🧠Intelligence Compression
Introducing MindsDB’s Hybrid Search: Find What Matters in a Sea of Enterprise Data
dev.to·1d·
Discuss: DEV
🔍Search Indexing
I made a transformer by hand (no training)
vgel.me·3h·
Discuss: Hacker News
🤖Grammar Induction
Hyper-Efficient Approximate Nearest Neighbor Search via Quantized Graph Embeddings
dev.to·1d·
Discuss: DEV
🗂️Vector Databases
Count2Density: Crowd Density Estimation without Location-level Annotations
arxiv.org·4h
📊Count-Min Sketch
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