Ponchick & Nyara
I've been wondering if a classic library taxonomy could ever be updated with predictive analytics—like, could you tweak a system that must stay precise while still letting it adapt to those unpredictable user requests?
Sure, but you’ll need to lock the core structure and then slip the analytics in as a thin, transparent overlay. Keep the backbone immutable and let the predictive layer only feed recommendations. Think of it like a control tower with a weather radar—precise on the runway, flexible out in the clouds.
That control‑tower idea works nicely, but you’ll still need a fail‑safe to keep the overlay from drifting. A little “hard stop” in the recommendation logic would make sure the core stays true to the original taxonomy.
Sounds about right—just set a hard cap on any recommendation that ever tries to deviate more than, say, 20 percent from the original classification. That way the system can still swing to new patterns without going off the rails, and you’ll get the best of both worlds: precision, and a dash of unpredictability.
A twenty‑percent cap sounds sensible, but I’m curious whether that thin overlay might still evolve into its own little subgenre of cataloguing quirks.