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Approximate Nearest Neighbor Search
HARMONY: A Scalable Distributed Vector Database for High-Throughput Approximate Nearest Neighbor Search
Published:6/18/2025
Approximate Nearest Neighbor SearchDistributed Vector DatabaseLoad Balancing and Communication OptimizationMulti-Granularity Partition StrategyHigh-Throughput Data Processing
Harmony is a scalable distributed vector database designed for highthroughput Approximate Nearest Neighbor Search, addressing load imbalance and communication overhead with a novel multigranularity partition strategy, achieving significant performance improvements in extensive
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Recommender Systems with Generative Retrieval
Published:5/9/2023
Generative Recommendation SystemsSemantic ID-based Recommendation ModelTransformer Sequence-to-Sequence ModelApproximate Nearest Neighbor SearchUser Behavior Prediction
This paper introduces a novel generative retrieval method using autoregressive decoding of Semantic IDs to enhance recommender system performance. A Transformerbased model effectively predicts the next item a user will interact with. Experiments show substantial improvements ove
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Optimized Product Quantization for Approximate Nearest Neighbor Search
Published:6/1/2013
Optimized Product QuantizationApproximate Nearest Neighbor SearchHigh-Dimensional Vector EncodingQuantization Distortion MinimizationParametric and Non-Parametric Methods
This study presents an optimized product quantization method to enhance the accuracy of approximate nearest neighbor search (ANN). By minimizing quantization distortions, two optimization approaches are proposed: a nonparametric method and a parametric method assuring optimal so
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Unleashing the Full Potential of Product Quantization for Large-Scale Image Retrieval
Published:11/2/2023
Deep Hashing Framework Based on Product QuantizationLarge-Scale Image RetrievalApproximate Nearest Neighbor SearchImageNet100 DatasetImageNet1K Dataset
This paper introduces a product quantizationbased deep hashing framework that addresses computational costs and accuracy issues in largescale datasets, efficiently learning classlevel codes through a differentiable PQ branch, demonstrating superior retrieval performance across
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