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Drifter: Efficient Online Feature Monitoring for Improved Data Integrity in Large-Scale Recommendation Systems
Real-world production systems often grapple with maintaining data quality in large-scale, dynamic streams.
Dynamic Surrogate Switching: Sample-Efficient Search for Factorization Machine Configurations in Online Recommendations
The main idea underlying this paradigm considers an incrementally updated model of the relation between the hyperparameter space and the output (target) space; the data for this model are obtained by evaluating the main learning engine, which is, for example, a factorization machine-based model.
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