BookNook: A book recommendation system Team members: Sonal.S.A Chandrashekar.H.L
vedio link = https://drive.google.com/file/d/14H68V16YRP36jgXd5aPkwY3qcYv_q7kR/view?usp=sharing
overview: BookNook helps you make the most out of your library by recommending books tailored to suit your reading tastes. This system is going to recommend books to the users on their level of interest organized and stored by maintaining record in data analytics using machine learning algorithms. The system will be implemented into the library's digital catalog, offering individualized recommendations at no extra charge. The entry will include user opinions and ratings, social media alternatives to share content online with pals or family members as well as notifications providing readers when fresh releases or popular titles go alive.
Core Features:
- Personalized Book Recommendations: o Description: The system analyzes users’ past borrowings, search queries, and reading patterns to suggest books they might enjoy. o Implementation: Utilize collaborative filtering and content-based filtering algorithms to generate recommendations.
- Genre-Specific Suggestions: o Description: Users can select their favorite genres, and the system will prioritize recommendations within these categories. o Implementation: Tag books by genre and use user-selected preferences to filter and recommend books within these genres.
- New Arrivals Notifications: o Description: Notify users about new books in their preferred genres or by their favorite authors. o Implementation: Monitor library database for new additions and send automated notifications based on user preferences.
- User Reviews and Ratings Integration: o Description: Allow users to rate and review books, and use this data to improve recommendation accuracy. o Implementation: Create a user interface for submitting reviews and ratings, and incorporate this data into the recommendation algorithms.
- Borrowing History Analysis: o Description: Use detailed analysis of users’ borrowing history to identify reading patterns and recommend books accordingly. o Implementation: Analyze borrowing frequency, reading speed, and genre preferences from historical data to tailor suggestions.