Practice — Case Study: Long-Term Engagement Recommender (5 questions)

A feed's current ranking score is score = 1.0*p(click) + 0.8*p(like) - 1.5*p(report). Leadership approves adding a long-term-value term driven by a 7-day session-return surrogate model, and accepts some near-term CTR cost.

1. Two engineers propose w5 = 0.05 and w5 = 1.0 respectively for the new term. Using the item-A/item-B style comparison from this case study (one high-click/low-surrogate item, one lower-click/ high-surrogate item), explain concretely why one of these choices would ship with no real behavior change.
2. What would you check, before launch, to confirm the chosen weight is "large enough to matter" without already needing the full long-horizon A/B result?
3. What guardrail would you put in place to catch the value formula accidentally collapsing near-term engagement to zero?