How We Evolved a Cultural Recommendation Feed From a Weighted SQL Ranker to a Narrative Affinity Model

Building a personalization engine for a multi-format content feed, without machine learning, and the testing process that forced us to rebuild it. TL;DR We run a collaborative cultural curation platform (think: user-submitted recommendations for movies, books, games, music, and long-form posts, all mixed into one feed) on a fairly ordinary PHP + MySQL stack. Over about a year we went through two full generations of the feed ranking algorithm. The first version solved the obvious problem (stop being purely chronological) but quietly failed at real personalization. The second version fixed that by rethinking what "user taste" even means, moving scoring out of SQL and into application code, and adding a layer of post-ranking business rules. This post walks through both generations, why the second one had to happen, and how we actually tested and calibrated a feed ranking system without a data science team or an ML pipeline. No exact weights, table names, or formulas below — just the engineering story. The starting problem: one feed, five content shapes Before personalization is even on the table, a multi-format feed has a normalization problem. Movies, books, games, music, and editorial posts live in different tables, with different columns, different publishing cadences, and engagement numbers on completely different scales. "1,000 likes" on a music post and "1,000 likes" on a book review are not the same signal. So the very first architectural decision — before any ranking logic existed — was building a unification layer that maps every content type into a shared shape (type, author, title, cover, category, engagement counters, timestamp) before any scoring happens. Everything downstream depends on that layer being consistent. Generation 1: a weighted ranker living inside a single SQL query The first real version of the algorithm — internally we called it the hybrid model — had a modest goal: get away from a purely chronological feed without building anything resembl