romack77

I agree. Collaborative filtering should succeed when you have sufficient of data, but we don't always have sufficient data. I think books are a tough challenge: the number of books is relatively large (vs. e.g. the number of restaurants on Yelp), the number of books people read is relatively small (vs. e.g. how many things they buy). Plus, people's tastes in them are relatively particular, and they're a big time commitment, so the bar for what counts as a "good" recommendation is high.

I think in these cases you can do better by bringing in some content-based filtering. I made an experimental book recommender using only story trope tags and I thought the results were already better than what I was getting elsewhere. It's still up at https://bookslikethis.herokuapp.com/ (but it basically only has sci-fi/fantasy titles).

The desire for these recommendation services not to be "too smart" really resonates with me. If we can't yet give a great set of zero-effort recommendations, why don't we pull back a step and give the user a few power tools to find their own? Maybe I'm out of step with mainstream users, but I would see that as a huge improvement.

Agreed. This is how I use Goodreads for recommendations as well. There's the "Compare Books" tool to get a quick sense of whether someone reads what you read. And then reading a couple of their reviews is usually enough to get a sense of whether their recs will agree with you.

It's more labor intensive than a standard recommendation system, but the results are better. Since books are a large time commitment, and people are passionate about them, a lot of people are willing to put uncommon effort into finding good ones.