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cs.IR2023
Drifter: Efficient Online Feature Monitoring for Improved Data Integrity in Large-Scale Recommendation Systems
Blaž Škrlj, Nir Ki-Tov, Lee Edelist +5
Real-world production systems often grapple with maintaining data quality in large-scale, dynamic streams. We introduce Drifter, an efficient and lightweight system for online feat…
cs.IR2023
OutRank: Speeding up AutoML-based Model Search for Large Sparse Data sets with Cardinality-aware Feature Ranking
Blaž Škrlj, Blaž Mramor
The design of modern recommender systems relies on understanding which parts of the feature space are relevant for solving a given recommendation task. However, real-world data set…