Catalogs are messy. A shopper clicks a lipstick because it is on discount, ignores a better product because the thumbnail is dull, buys a cable for someone else, and later returns to search for something completely unrelated. A recommender system sees all of this as signal. Some of it is useful. Some of it is noise wearing a very confident jacket. ...
A list of individually excellent items can still be a terrible list. Ask anyone who has attended a conference with five brilliant speakers, no agenda, and three consecutive sessions on the same topic. Recommendation systems have the same problem. A conventional recommender can assign highly accurate scores to individual videos, products, or articles, then still assemble a repetitive, badly ordered, or strangely balanced feed. Each item wins its private competition. The user receives the collective consequences. ...