The Problem
Recommendation looks like supervised learning and behaves nothing like it. The data is almost entirely missing, the missingness is not random, the output is a ranked list rather than a prediction, and the model's own outputs determine what data you collect next.
Collaborative Filtering
Collaborative filtering makes recommendations from interaction patterns alone. It never looks at what an item is — only at who engaged with it. That is simultaneously its great strength and the source of its cold-start failure.
Content-Based & Hybrid
Collaborative filtering cannot recommend an item nobody has touched. Content-based filtering can, because it looks at what the item is rather than who engaged with it — and every production system ends up combining the two.
Evaluation
Recommender evaluation is unusually treacherous: the offline metric is computed on data generated by a previous model, the ranking metrics disagree with each other, and the correlation between offline improvement and online business impact is famously weak.