Context

StreamCart uses a binary relevance model to rank and filter product search results for its mobile app. The team recently tightened the decision threshold to reduce irrelevant results, but user complaints about “missing obvious items” increased while support tickets about “spammy results” fell slightly.

Current Performance

The Problem

The product manager wants to understand the difference between precision and recall in terms of user experience, and whether the current model is actually better despite higher accuracy and precision. You need to explain what these metrics mean for shoppers, diagnose the tradeoff, and recommend what to optimize next.

Requirements

1. Explain precision vs. recall using this search experience.
2. Interpret why higher precision may still produce a worse user experience here.
3. Use the metrics and confusion matrix to identify the main failure mode.
4. Recommend threshold or model changes and how you would validate them.

Constraints

• StreamCart prioritizes search satisfaction over small gains in moderation efficiency.
• Showing some irrelevant products is acceptable; hiding relevant products is more damaging.
• Any change must keep latency under 120 ms at p95.