How to Run A/B Tests for Site Search: Metric Design and Practice
A/B test site search changes: split into recall, ranking, and result-page layers; search conversion rate primary, zero-result and first-click rates guardrails.
A/B test site search changes: split into recall, ranking, and result-page layers; search conversion rate primary, zero-result and first-click rates guardrails.
Collaborative filtering: UserCF for content platforms, ItemCF for stable e-commerce. A 5-dimension comparison, co-occurrence and content-vector blending.
Recommendation ROI comes from scenarios, not one big model. We rank eight high-ROI e-commerce scenarios by rollout difficulty, with data and metrics for each.
Recommendation placement design: pick the page, content, and attention share. Match each slot to page intent and measure cannibalization to confirm real gains.
How to design a membership program? Set the goal first, then structure: points drive behavior, tiers segment users, benefits deliver value, each owning one job.
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