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@article{178061,
        author = {HARSHIT CHAUDHARY and ASHWANI SINGH and NAVEEN GUPTA and ANKIT MISHRA},
        title = {Hybrid Book Recommendation System: Combining Popularity with Collaborative Filtering},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {3721-3725},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178061},
        abstract = {Recommendation systems are software tools designed to deliver personalized suggestions based on user preferences, behaviour, and interactions. With the rapid growth of digital books, there is a pressing need for efficient systems to assist users in navigating large collections. This paper examines various book recommendation methodologies, highlighting their advantages and limitations, and explores ways to improve accuracy and user satisfaction. By synthesizing insights from recent research and applying advanced machine learning techniques, this study proposes a hybrid system that integrates popularity-based filtering, collaborative filtering, and cosine similarity measures. The developed model aims to provide reliable, scalable, and user-centred recommendations for enhanced user experience.},
        keywords = {Recommender System, Popularity-Based Filtering, Collaborative Filtering, Cosine Similarity, Hybrid Model},
        month = {May},
        }