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@article{169940,
        author = {Pawansai muppidwar and Anup meshram and Yashwant patil and Aman nagrale},
        title = {Book recommender system},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {2827-2830},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169940},
        abstract = {The subscribers in this era of digital information have continuously relied on book recommender systems as tools that aid readers in selecting books that complement their interests, preferences, and reading nature. The motivation of this paper is to build a book recommender system which suggests appropriate book to a user by utilizing a combination of collaborative filtering, content-based filtering and hybrid recommendation techniques. In this system, user data such as reading history, ratings, and preferences of a user as well as the book’s metadata including genres, authors and reviews, will be used to generate recommendations. More sophisticated algorithms such as matrix factorization and deep learning will be embedded into the system’s architecture to mitigate the effects of sparse data and enhance contextual recommendations. User perspectives have also been taken in so as to optimize the recommendation process over time. Such a recommender system’s goal is to solve the problems of information overload by providing readers with numerous technologies which ushers in contact between the vast number of books and the reader, thus enabling a more fulfilling and personalized experience.},
        keywords = {book metadata, collaborative filtering, personalized recommendations, reading-preferences.},
        month = {November},
        }