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Posted11 days ago
Key Takeaways
Do you mean one should post their reviews of last 10 books read into Gemini and then ask it to find 20 rare-gems books based on the content of those reviews?
We just launched a new feature that lets you enter a book/author you love and see which books readers who also loved that book/author liked as part of our "3 favorite reads" of the year poll.
Try it out here: https://shepherd.com/bboy/2025
What do you think?
We are also building a full Book DNA app, which pulls in your Goodreads history and delivers deeply personalized book recommendations based on people who like similar books.
You can sign up to beta test it here if you want to help me on that: https://docs.google.com/forms/u/1/d/1VOm8XOMU0ygMSTSKi9F0nEx...
The first beta is coming out in late January, but it's pretty basic to start. Very early preview here as we build it: https://www.youtube.com/watch?v=BUMJ6uLNfjM&feature=youtu.be
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.
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.
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?
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