Show HN: I scraped 3B Goodreads reviews to train a better recommendation model

For the past couple months I've been working on a website with two main features:

- https://book.sv - put in a list of books and get recommendations on what to read next from a model trained on over a billion reviews

- https://book.sv/intersect - put in a list of books and find the users on Goodreads who have read them all (if you don't want to be included in these results, you can opt-out here: https://book.sv/remove-my-data)

Technical info available here: https://book.sv/how-it-works

Note 1: If you only provide one or two books, the model doesn't have a lot to work with and may include a handful of somewhat unrelated popular books in the results. If you want recommendations based on just one book, click the "Similar" button next to the book after adding it to the input book list on the recommendations page.

Note 2: This is uncommon, but if you get an unexpected non-English titled book in the results, it is probably not a mistake and it very likely has an English edition. The "canonical" edition of a book I use for display is whatever one is the most popular, which is usually the English version, but this is not the case for all books, especially those by famous French or Russian authors.

Show HN: I scraped 3B Goodreads reviews to train a better recommendation model
book.sv

* UI - once someone clicks "Add" you really should remove that item from the suggested list - it's very confusing to still see it.

* Beam search / diversification -- Your system threw like 100 books at me of which I'd read 95 and heard of 2 of the other 3, so it worked for me as a predictor of what I'd read, but not so well for discovery.

I'd be interested in recommendations that pushed me into a new area, or gave me a surprising read. This is easier to do if you have a fairly complete list of what someone's read, I know. But off the top of my head, I'm imagining finding my eigenfriends, then finding books that are either controversial (very wide rating differences amongst my fellow readers) or possibly ghettoized, that is, some portion of similar readers also read this X or Y subject, but not all.

Anyway, thanks, this is fun! Hook up a VLM and let people take pictures of their bookshelf next.