The Algorithmic Reader: How Recommendation Systems Influence Literary Taste

In the digital age, literary consumption is no longer guided primarily by critics, teachers, or personal exploration—but by algorithms. These recommendation systems, embedded in platforms such as Amazon, Goodreads, and TikTok, shape what readers see, what they choose, and ultimately what becomes “taste.” This paper argues that algorithmic curation is transforming literary taste in three interlinked ways: first, by reinforcing existing preferences and narrowing exposure; second, by shifting the power of discovery from the reader to data-driven systems; third, by changing how value and originality are perceived in literature. Drawing on case studies of BookTok’s influence on Generation Z, Goodman’s theories of taste (via Bourdieu), and empirical studies of reading challenges and digital literacy, the paper shows that while recommendation systems democratize access to books, they also homogenise what becomes visible, valued, and read. The conclusion reflects on what this means for readers, writers, and the broader literary culture: that literary freedom is becoming a negotiation with invisibility, algorithmic scaffolding, and predictive expectation.