ShelfSense

ShelfSense is a mobile + web AI recommendation app that helps readers pick their next book based on what they actually finish, not what they merely rate. Users connect Kindle/Apple Books/Goodreads exports (or upload a simple reading log) and the app learns completion patterns: pacing tolerance, preferred chapter length, genre-mixing, and “drop-off triggers” (e.g., slow starts, dense prose). It then recommends books with a clear why: expected enjoyment, predicted completion likelihood, and the specific traits that match the user’s habits. The MVP focuses on a tight loop: import history, get 10 high-confidence picks, and track outcomes to improve. This is not trying to beat Amazon’s catalog-scale recommender; it’s a personal “finishability” engine that saves time and money for heavy readers and book-club members who are tired of hype-driven picks.