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Filter-then-Score Recommendation Pipeline

concept 2 connections

Two-stage architecture the Nebulab team adopted after rewriting their Cometeer recommendation code. Stage 1 (filtering): start with the full product pool and sequentially apply small filters (matching roast level, caffeine level, inventory, customer dislikes, etc.) to narrow candidates. Stage 2 (scoring): for each surviving product, run an arbitrary number of weighted amplifiers that multiply a running score based on local conditions (votes), then pick the top-scoring product. When no winner exists, the product pool is too small — treated as a supply-chain problem rather than an engineering one. Top competitors are stored as metadata so stakeholders can ask 'if not this one, what would have been picked next?'

category
architecture
about
Filter-then-Score Recommendation Pipeline concept
The rewrite adopts an explicit filter stage followed by a weighted scoring stage.
concept Filter-then-Score Recommendation Pipeline
related_to
Neural Network concept
The filter and scoring stages map to hidden layers of a neural network.

Provenance