I read a lot, and I want personalized book recommendations from other people based on my favorite books, authors, and genres. So, I created this tool using data we've been collecting for the last three years at Shepherd.com (which launched on HN three years ago).

The tool takes in 3 of your favorite books/authors and 3 of your favorite genres/topics.

Then, it shows you nine book lists based on your favorites to see if one resonates. Every book list on the website is made by an author or expert, so they either have passion or expertise (fiction or nonfiction).

And, you can sign up for a weekly email with new book ideas based on this. The email system is 100% personalized, so every person gets a 100% customized email every week—there is never a duplicate list.

Here is an example using my favorites:

https://shepherd.com/my-book-dna?r=books%3A34472&r=books%3A7...

I still have more to do here:

- Improve genre/topic accuracy; I am working on that this winter.

- I am working to launch a Book DNA format to try to decode why you loved a book and better match you with books based on similar readers.

- And generally improving this email as I get feedback :)

Shepherd is bootstrapped. I have a newsletter about building it and early access to new features here: https://build.shepherd.com/

What do we use to build this? Python, Django, Heroku, Postgre, Cloudflare, Postmark for email, NLP/ML for Wikipedia topic IDs via Wikifier (https://wikifier.org), Nielsen’s book API database (publisher data + Library of Congress data + BISAC), and Cloudinary.

My email is [email protected] if you want to share ideas or suggestions :)

Thanks, Ben