There's tons of material online but yet I can't reconcile the different definitions for recommender system methodologies / strategies.I think we can identify several axes: memory vs model based; Model based models do not need to store historical data such as user ratings or proxies (implicit data) such as purchase behavior, views, etc. product based vs collaborative filtering, aka user-based filtering. Collaborative filtering relies on user preferences data (implicit or explicit - explicit when users explicitly provide ratings, likes, stars). Product based rely uniquely on product features.collaborative filtering comprises item based, which is totally based on user-item rankings, and content-based recommendation, which exploits product features and user preferences (although other sources put content based filtering outside the family of collaborative filtering)Is this correct? Anything (big) missing?
Taxonomy of recommender system methodologies
recommender system