Researchbloom filterfeature storerecommender systemssketching
Sketching Feature Stores Reduce Memory And Latency
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6.9
This article demonstrates using bloom-filter-backed sketching feature stores to compress and serve historical categorical features for recommender ML. Using a real click-prediction dataset (5.7 million examples, 1.762 billion extracted data points) and logistic regression, the compressed store achieved similar accuracy and throughput while fitting state in-process, reducing external lookups and operational costs.
Scoring Rationale
Practical benchmark and measurable latency/memory benefits drive score; limited novelty and single-source validation constrain broader adoption.
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