content-based-filtering plugins

by Matt Corbett

Recommendation-systems-engineering team — the RECSYS layer that turns interaction data into ranked recommendations. agents (recsys-architect, recsys-implementation-engineer) covering approach selection (popularity baseline, collaborative filtering, content-based, hybrid, two-tower/embedding retrieval, sequential), the candidate-generation → ranking → re-ranking pipeline, cold-start (new user/new item), offline evaluation (recall@k, nDCG, MAP, coverage/diversity) AND online A/B, feature stores with train/serve parity, low-latency serving, and feedback-loop/position bias. skills, Mermaid-backed knowledge docs, templates. Seams: training infra → ml-engineering; keyword/semantic search → search-relevance-engineering; A/B + stats → experimentation-growth-engineering + applied-statistics. House line: baseline before a neural net; offline wins must survive an online A/B. Requires ravenclaude-core@>=0.7.0.