This repository contains the code for a book recommendation system that uses natural language processing techniques to recommend books to users based on their preferences.
The dataset used in this project is a collection of books from various genres, including fiction, non-fiction, romance, thriller, etc. Each book is represented by a set of features, including the author, publisher, publication date, and user ratings.
The book recommendation system uses a content-based, collaborative filtering approach to recommend books to users. It leverages natural language processing techniques, such as matrix factorization, association rules, and topic modeling, to extract features from the books and user data.
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In the initial phase, the system recommends books to users based on their preferences using a content-based approach. It matches the features of the books with the user preferences and recommends books that match their preferences.
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In the second phase, the system recommends books to users based on their similarity with other users. It uses a collaborative filtering approach to recommend books based on the preferences of similar users.
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Finally, the system uses a hybrid recommendation approach that combines the results of both content-based and collaborative filtering approaches to provide the most relevant recommendations to users.
The book recommendation system is implemented in Python using various natural language processing libraries such as NLTK, Scikit-learn, and Gensim. The system also uses various machine learning algorithms, such as matrix factorization, association rules, and XGBoost, to extract features and recommend books to users.
The book recommendation system is an efficient way of recommending books to users based on their preferences. It uses various natural language processing techniques and machine learning algorithms to extract features and recommend books to users. The system can be further improved by incorporating user feedback and providing more personalized recommendations to users.