That's the simplest and most naive implementation of a recommender. It doesn't take into account what I liked about a book and whether the one it's recommending is similar in that way or if I will dislike it for some other reason.

A better way to do recommendations is to find readers who have rated books similarly to me and tell me what books they've rated highly that I haven't already read. This is what Netflix's Cinematch once did and it was good. It used singular value decomposition and other linear algebra techniques on a giant matrix of raters.

See: https://www.netflixprize.com/

I have no expectation of ever seeing useful recommendations from the sites I frequent. This might be heresy on HN, but by and large recommendation engines are pure trash. Have you ever been recommended a useful product on Amazon? One would think that a company with such large resources could occasionally point me to a product I might want.

> doesn't take into account what I liked about a book

I don't remember ever seeing a rating system (for books or tv) that let me do fine grain rating. And if it had it, most people[citation needed] might not use it anyway. Also, how would I say that what I liked the most about a fantasy book is the magic system, for example?

I don't care HOW the recommendation is implemented, but what shows on the recommendation. For me, 'Readers also enjoyed' shows things that are sufficiently similar, and which people that I follow - friends and bloggers - gave good ratings.

The 'lists with this book' section is also good for discovering books, as the lists are user curated, and the title helps me know what's the common theme. Ie: books with cool magic systems.

That's the beauty of this. You don't need a fine grained rating system. By comparing your preferences to those of others, the aspects of what you like and don't like become implicit.

A product has features in some high dimensional quality space. When you rate it, you provide information about your preference for those qualities without needing to explicitly rate them or even have a concept of what they are called.