Closed-Form Related-Content Recommendation — with an Honest Relevance Gate

Abstract

Every article in the Journal of the Commons carries, in its record, a short list of related work drawn from the entire catalogue — latest and archived. We build that list in closed form, in the spirit of Reverse Synthetic Networks (RSN): the corpus's own statistics synthesise each article's neighbours directly, with no trained model, no embedding service, no gradient, and no per-user state. Relatedness is a TF-IDF cosine over each article's title, abstract and keywords, refined by a few structural signals (shared field, shared keywords, shared authors, explicit implements/implementation links). On a controlled benchmark of 30 random corpora with known topical ground truth, the engine reaches nDCG@10 = 0.964 ± 0.005 and Precision@5 = 0.994 ± 0.004 (mean ± sd), against 0.500 for a field-matching baseline and ≈0.08 for content-blind baselines. Crucially, a relevance gate derived from the corpus's own similarity distribution makes the engine return nothing for an article that has no genuine relatives: across 30 corpora seeded with one-of-a-kind articles, isolated articles are correctly suppressed 100% of the time (false-positive rate 0.000), while articles that do have relatives are served 100% of the time at 99.4% suggestion precision. Ranking 5,000 articles takes 177 ms on one CPU core.