This project uses machine learning to create a personalized bookrecommendation system. By combining collaborative filtering and content-based filtering, it analyzes user preferences and book attributes to suggest tailored book recommendations. The system offers real-time updates and accurate predictions to enhance the user experience.
Files uploaded is the explaination of the mathematical concepts used in the book recommendation system that how the Cosine Similarity concept is used in recommendation systems
This subsets of the data is matched and recommended based on the similarities and dissimilarities of the book
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0(theta) -> 0* (Zero degree) -> similarity is equal or near to 100 %
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0(theta) -> 90* (90 degree) -> similarity is equal or near to 0 %
This percentage od the books is recommended based on the subsets like book_content, book_title, author, & average_rating.