Personalized Book Recommendation Engine With Collaborative Filtering

Added Jun 2025 3 design docs

Readers abandon platforms that keep recommending books they have already read or would never enjoy, and small education and entertainment products rarely have the recommendation muscle of large retailers. A well-built recommender that learns from reading history can noticeably lift engagement even with modest amounts of data.

The intern builds a Flask web application in Python where users create a profile, log the books they have read, and rate them. A collaborative filtering engine built with core data science libraries finds users with similar tastes and surfaces titles those readers loved, while simple content-based signals like genre and author fill the gap for new users with little history. The interface presents each recommendation with a short explanation of why it was chosen, and the intern evaluates quality properly, using train-test splits and precision-style metrics rather than eyeballing results.

The project demystifies how recommendation systems actually work: representing user-item interactions as matrices, computing similarity between readers, handling the cold-start problem, and shipping machine learning inside a real web product that a school or reading community could adopt.

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