How Amazon and Netflix predict your preferences and prod you to purchase

This is the main problem with automated curation:

The user-user and item-item algorithms have a bigger problem than consistency: They’re too rigid. That is, they can spot people who prefer the same item but then miss potential pairs who prefer very similar items. Let’s say you’re a fan of Monet’s water lily paintings. Of the 250 or so paintings of water lilies that the French impressionist did, which is your favorite? Among a group of Monet fans, each person may like a different water lily painting best, but the basic algorithms might not recognize their shared taste for Monet.

I think the key is serendipity:

Serendipity rewards unusual recommendations, particularly those that are valuable to one user but not as valuable to other similar users. An algorithm tuned to serendipity would note that the “White Album” appears to be a good recommendation for nearly everyone and would therefore look for a recommendation that’s less common—perhaps Joan Armatrading’s Love and Affection. This less-popular recommendation wouldn’t be as likely to hit its target, but when it did, it would be a much happier surprise to the user.