BookReading
Get a reading recommendation based on actual reading history, not a black-box algorithm.
A reading list spread across physical books, ebooks, and audiobooks had no unified tracker, and Goodreads recommendations felt arbitrary.
A public prototype demonstrates the interaction; scope and data are intentionally limited.
Case Study
Before
Managing a growing reading list across multiple formats (physical, ebook, audiobook) with no unified progress tracking or recommendation system. Goodreads recommendations were algorithmically opaque.
After
Created a transparent recommendation system that suggests books based on actual reading patterns. Progress tracking across formats provides a unified view of reading activity, replacing fragmented tracking methods.
How it's solved
Built a reading platform with AI-powered recommendations based on reading history and preferences, progress tracking across formats, personal library management, and reading goal setting. Python/FastAPI backend handles the recommendation engine.
Trade-offs
Recommendations are based on this reader's own history — not a broader collaborative-filtering dataset.
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