BACO compresses recommender embedding tables by over 75% using balanced co-clustering of users and items that maximizes intra-cluster connectivity while enforcing size balance, with at most 1.85% recall drop and up to 346X speedup over baselines.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
SRast is a generalist framework using self-supervised decoupling of gene and spatial representations plus flow matching for physically consistent super-resolution of spatial transcriptomics data with strong zero-shot generalization.
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Balanced Co-Clustering of Users and Items for Embedding Table Compression in Recommender Systems
BACO compresses recommender embedding tables by over 75% using balanced co-clustering of users and items that maximizes intra-cluster connectivity while enforcing size balance, with at most 1.85% recall drop and up to 346X speedup over baselines.
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Towards Universal Spatial Transcriptomics Super-Resolution: A Generalist Physically Consistent Flow Matching Framework
SRast is a generalist framework using self-supervised decoupling of gene and spatial representations plus flow matching for physically consistent super-resolution of spatial transcriptomics data with strong zero-shot generalization.