Dev Team Rebuilt a Cultural Feed Ranker Twice — Without ML or a Data Science Team

A collaborative cultural curation platform running on PHP and MySQL spent roughly a year iterating through two generations of its content feed ranking algorithm. The first version moved beyond chronological ordering by combining popularity, recency, and basic genre affinity into a single SQL-based score, but testing revealed that personalization was largely invisible due to a content-surfacing rule dominating the first page. The team rebuilt the system by shifting scoring logic from SQL into application code and rethinking how user taste is defined, ultimately creating what they call a narrative affinity model. Without a data science team or machine learning pipeline, calibration relied on synthetic user profiles, first-page composition audits, and repeated observation of real feed output. The engineers also solved a foundational normalization problem first, mapping movies, books, games, music, and editorial posts into a unified data shape before any ranking logic could be applied.