How do you test and validate a new recommendation algorithm before full-scale deployment?
Instruction: Describe the process for ensuring a new algorithm's effectiveness and readiness for deployment.
Updated
Example Answer
I would start with reproducible offline evaluation, using an appropriate time split, candidate set and relevance definition. Historical logs are not a live randomized A/B test. Offline policy evaluation can sometimes estimate a new policy from logged actions, but needs suitable exploration or propensities, support and estimator assumptions. After operational checks, a bounded randomized online experiment can measure live impact. I would predefine outcomes, guardrails and stopping rules, then stage deployment with rollback.
Make it your own
Illustrative readiness artifact: leakage checks, replay/serving consistency, logging completeness, experiment assignment, error/latency guardrails and a rollback owner.
Why this works
The stages produce different evidence and should not be conflated. Follow-up: why can a policy recommending previously unexposed items be hard to evaluate from old logs?
Sources: Offline contextual-bandit evaluation research, Microsoft trustworthy experiment design, scikit-learn model evaluation.