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Project Overview
This Book Recommendation System utilizes advanced Natural Language Processing (NLP) techniques to build a content-based recommendation engine for books. By analyzing the textual content of Darwin’s books, this system ranks how closely related each book is to others based on shared subject matter.
Key Features:
- Content-Based Recommendation: Recommends books based on the similarity of their content, allowing users to discover related books based on the subject matter.
- TF-IDF Model: Utilizes the Term Frequency-Inverse Document Frequency (TF-IDF) model to weigh the importance of words in the text, improving the relevance of book recommendations.
- Cosine Similarity: Measures the similarity between books based on their content, calculating how close each book is to another in terms of subject matter.
- Book Ranking: Ranks books by relevance to the input book, enabling users to explore books that are most similar in content.
Technologies Used:
- Natural Language Processing (NLP): For analyzing the text and extracting meaningful insights.
- Bag of Words Model: Converts text into a structured format for processing and comparison.
- TF-IDF Model: Used to assess the significance of words in relation to the entire collection of books.
- Cosine Similarity: Computes the similarity between books based on their content.
Use Cases:
- Bookstores and Libraries: Implement a recommendation engine to suggest books based on customer preferences.
- E-commerce: Enhance book recommendation systems to drive sales and improve customer experience.
- Researchers & Educators: Find related literature and books for academic purposes.
- Reading Enthusiasts: Discover books that match personal interests or delve deeper into related subjects.