Design experiments that measure true recommendation quality, avoid common pitfalls, and iterate effectively.
A/B testing recommendations requires careful metric selection, user-level randomization, sufficient sample sizes, and awareness of feedback loops. Key metrics include CTR, conversion, revenue, and diversity. Interleaving experiments detect differences faster than traditional A/B tests.
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