#65 - Book Vibes
Unlocking Book Recommendations with Vibes: An Inside Look at the Future of Reading Discover how hardcover is revolutionizing book discovery through its innovative recommendation system called Vibes. In this episode, we explore the technology behind personalized book suggestions, how Vibes creates tailored recommendations, and what’s coming next for this powerful tool. Key Topics: - The concept of book Vibes: understanding the fingerprint of books based on reading data - How Vibes uses a recommendation engine powered by vector databases and machine learning models - Behind the scenes of generating book vectors and how cosine similarity finds similar reads - The process of training the model with millions of books and the importance of probabilistic genres - How user preferences and collaborative ranking refine recommendations - The role of genre data, axioms, and World types in shaping book suggestions - Excluding books and authors from recommendations and the impact of user feedback - Plans for expanding Vibes’ features, including API access and customization options - Visualizing vectors and genres to better understand book relationships - Future ideas for recommending based on user book clubs, want-to-read lists, and more Timestamps: 00:00 - Introduction to hardcover's new recommendation feature, Vibes 02:10 - How hardcover knows the data behind your reading habits 03:39 - Creating an advanced book search system that finds what you don't know 04:36 - Building a recommendation engine to match niche book preferences 05:04 - Upcoming features and API integrations for Vibes 06:30 - Exploring trending and personalized recommendation Vibes 11:27 - How Vibes generates up to 250 book recommendations for your taste 12:23 - The technical backend: vector databases and cosine similarity in recommendations 14:45 - Training a two-tower model for book vectors and genre understanding 16:16 - Incorporating user collaborative data for personalized suggestions 17:02 - Experimenting with signals and signals weights to refine recommendations 18:21 - The role of popularity weights and social signals in suggestion quality 19:50 - Handling classic and crossover genres like Pride and Prejudice 22:23 - Why different genres and classic books show up in recommendations 25:13 - Visualizing vectors and the potential for future genre space exploration 26:46 - How axioms and world types help refine genre and book classification 37:52 - Building the system from scratch: the development process and lessons learned 49:16 - Feedback, tuning, and future improvements for Vibes 58:54 - Making recommendations accessible for all users and future API plans 60:41 - Closing thoughts: the vision for discovering books that match your unique taste This episode offers a comprehensive look at the technology shaping personalized book discovery. Whether you're a niche reader or just love finding your next great read, learn how hardcover’s Vibes system can help you explore books in ways you never thought possible.





