mohsenMahmoodzadeh/book-crossing-recommender-system
A python notebook for building collaborative, content-based, and ml-based recommender systems with Sklearn and Surprise
This helps online booksellers create personalized reading suggestions for their customers. By analyzing customer ratings and book details, it generates lists of recommended books tailored to individual preferences. The ideal user is an e-commerce manager or data analyst at an online bookstore looking to enhance customer experience and boost sales through targeted recommendations.
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Use this if you manage an online book-selling platform and want to offer intelligent, data-driven book recommendations to your users.
Not ideal if you need a recommendation system for items other than books, or if you require advanced deep learning models for recommendations.
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Apr 20, 2023
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