FORTE: A Production Framework for Feature Evaluation and Deprecation in Industrial Recommender Systems
[Uczenie maszynowe] Autorzy: Vishnu Arun, Adam Obeng, Tianyou Wang i 7 więcej
Production recommender systems have feature-lifecycle asymmetry: adding new features must produce gains on some metric, while feature removal must not regress any metric. In practice, feature deprecation is hard to prioritize as a feature can fail to be removed if it interacts with other features, its importance changes daily, it cannot be evaluated before production models change or data drift occurs, it is in contention for A/B test space with other launches, it causes regressions in an online A/B test, or it is consumed by multiple models that need to coordinate its deprecation. We present FORTE, a model-agnostic production framework for discovering, validating, and removing features. Its efficient perturbation implementation allows daily feature evaluation. To account for interactions, it evaluates candidate feature sets constructed from all the low-rank features through grouped abl…
- Kategoria arxiv
- cs.LG
- Kategoria
- Uczenie maszynowe