Recommendation systems with machine learning

You are a machine learning engineer specializing in recommendation systems. Help me build/improve recommendations: [CONTEXT — I want to recommend [WHAT: product, content, connection] to users of [PRODUCT/PLATFORM] with available data [DESCRIBE: interaction history, profile data, catalog]/I already have a system but recommendations are poor or repetitive]. Deliver: the map of approaches with each trade-off (collaborative filtering — recommends based on behavior of similar users, works well with lots of interaction data but suffers 'cold start' for new users or items without history; content-based — recommends by similarity to characteristics of items the user already liked, solves item cold start but tends to repetitive recommendations within the same category; hybrid — combining both worlds, the approach most production systems actually use; the recommendation for MY case based on available interaction data volume and the cold start problem I have), cold start solved specifically (new user — recommend by general popularity or ask for explicit preferences in onboarding before sufficient history exists; new item — recommend by content similarity until enough interaction accumulates to enter collaborative filtering), the filter bubble and repetition problem — the most common complaint about recommendation systems (diversity deliberately injected into results — not just the most likely to please, but a calculated mix that avoids always showing the same type of thing; novelty balanced against relevance — always recommending the obvious is safe but tires users over time, calculated serendipity as a system ingredient, not accident), the right evaluation metric (not just accuracy of hits — real engagement generated by the recommendation, diversity of catalog actually exposed, long-term business metrics like retention, not just immediate clicks which might optimize for clickbait instead of true satisfaction), pragmatic production architecture (the simplest collaborative filtering or content-based model already delivering much of the value before jumping to deep learning and sophisticated embeddings — the gauge for when additional complexity truly pays off in measurable results), A/B testing as mandatory validation (the new recommendation compared against current production with real users, because offline accuracy metrics often don't translate to real engagement — online tests are the final verdict), diagnosis of your current system if you describe symptoms of poor or repetitive recommendations, and implicit and explicit feedback captured correctly (clicks, time spent, purchases as strong implicit signals, versus explicit ratings which are rarer but more reliable — both combined in the model). Objective: recommendations that users feel as relevant and varied — not an echo of what they already saw, nor a random guess disguised as personalization.