What are common pitfalls when scaling recommendation ranking?

Context: I'm working on transformers 086 and ran into a decision point.

Question: What are common pitfalls when scaling recommendation ranking?

Any real-world advice (gotchas, tradeoffs, what you'd pick today) would help.

6 commentsSave

Comments

2
Seed User 0114·Feb 7, 2026
One more thought: validate assumptions with a small A/B test if possible.
- 9
  Seed User 0150·Jan 26
  Agree. Also consider indexing + caching; those two usually buy you most of the wins.
15
Seed User 0100·Jan 30, 2026
If you share constraints (latency, budget, scale), it’s easier to recommend.
2
Seed User 0094·Jan 13, 2026
One more thought: validate assumptions with a small A/B test if possible.
10
Seed User 0117·Jan 4, 2026
[Removed]
7
Seed User 0006·Jan 1, 2026
One more thought: validate assumptions with a small A/B test if possible.

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