Smart Book Recommendation System with AI

With millions of books available, finding the perfect read can be overwhelming. Traditional recommendation methods like bestseller lists and manual suggestions often fail to match individual interests.

The Smart Book Recommendation System leverages AI and collaborative filtering to provide personalized book recommendations based on user interactions, ratings, and reading habits. It refines suggestions by identifying users with similar preferences, making book discovery more engaging and efficient.

smart book recommendation system technology

Technologies Used

Programming Language

Python

Front-End

React.js

Platform

Cloud-Based

Database

PostgreSQL

Web Server

Flask

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Requirement

A vast collection of books makes it difficult for readers to find content tailored to their interests. Conventional recommendation methods lack personalization, leading to inefficient book discovery.

An AI-driven recommendation engine can analyze user behavior, preferences, and interactions to suggest books that match individual tastes, enhancing the reading experience.

Solution

The system applies collaborative filtering and data clustering to recommend books based on user preferences.

User Similarity Analysis – Finds users with similar reading habits.
Data-Driven Insights – Analyzes ratings, book attributes, and browsing behavior.
Efficient Data Processing – Merges large datasets for precise recommendations.
AI-Powered Predictions – Uses machine learning to enhance accuracy.

Final Outcome

The Smart Book Recommendation System transforms book discovery by offering customised recommendations that improve user engagement and reading satisfaction. It eliminates guesswork, ensuring readers always find books that match their interests.

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