Improve book discovery by modeling a reader's unique taste from rated history

Improve book discovery by modeling a reader's unique taste from rated history targets a $2.0B = 40M avid readers × $50 ACV (annual willingness-to-pay for discovery/personalization tools) total addressable market with medium saturation and a year-over-year growth rate of 12% — personalization and digital reading discovery tools are growing as eBook/audiobook consumption rises (industry estimates, Statista/industry reports 2024-25).

Key trends driving demand: Personalization advances — better embeddings and fine-tuned recommendation models make matching niche tastes practical and measurable, creating an opportunity for superior discovery.; Privacy-aware portability — users can export Goodreads/book histories, enabling integration without scraping or vendor cooperation and making migration easier.; Growth in digital formats — increased audiobook and eBook consumption expands the touchpoints where personalized recommendations can be useful.; Subscription fatigue — readers are looking for focused, high-value subscriptions or micro‑subscriptions that improve discovery rather than adding noise..