Book recommendations based on reading history

One of the services that I know is Shepherd https://shepherd.com. The service is like Goodreads, but it's more personalized. Shepherd is trying to help the readers to discover and share the books. I am not the author. You can read the details on https://build.shepherd.com/p/what-is-shepherds-mission-for-r....

Last but not least, this is the example of someone's favorite books https://shepherd.com/bboy/2024/f/william-hansen.

You might like https://abooklike.foo/.

It’s an interesting challenge. Modern recommendation systems grew powerful because of enormous amounts of instant feedback. You can capture clicks and view time on the web. You don’t get that in books.

I see three possible solutions:

1. Google approach: scrape the web for book recommendations and somehow create an ML recommendation system that’s better than Goodread’s 2. Pandora Radio approach: (semi-)manually create classifiers for books (genre, tone, character traits, etc.) and build a recommendation system with that. 3. Practical approach: find book reviewers whose opinions you trust and follow their recommendations.

I thought https://book.sv was pretty good. It was on HN recently: https://news.ycombinator.com/item?id=45825733. When I inputted 5 books I liked, the recommendations were a combination of:

1. books I had already read and enjoyed before

2. books that were already on my list (either from friends or other recommendations)

3. books I hadn't heard of

That said, I haven't read a book from #3 yet, so I can't fully vouch for it, but #1 and #2 are positive signals to me.

Once you cross 100 books its all repetition. Just like HN comments.

You might try this: https://shepherd.com/my-book-dna

This is an early beta as we work on this problem; I want deeply personalized book recommendations for similar readers.

What do you think of the results?

Paste in last 10 reviews to Gemini or gpt and ask for 20 "rare-gems, unique and exquisite," with descriptions. Works well