Towards Context Aware and Age-Based Book Recommendation System using Machine Learning for Young Readers

In an era characterized by an overwhelming abundance of digital content, the significance of personalized book recommendations for young readers cannot be overstated. This research paper introduces a pioneering Content-Based Age-Wise Book Recommendation System, which tackles the challenge of tailoring book suggestions to young audiences. Our system harnesses the power of K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, and Naive Bayes techniques to provide intelligent book recommendations based on content while taking into account the user's age category. The paper embarks on a comprehensive exploration of the system's development journey, commencing with data acquisition and preprocessing to ensure the dataset's quality and consistency. Through the utilization of the TF-IDF method, the textual content of books is translated into numerical features, enabling precise content-based analysis. A pivotal aspect of this research involves age group segmentation, a critical component that paves the way for personalized recommendations catering to distinct age categories, including children, teenagers, and young adults. Our research delves into the intricate workings of KNN, which measures book content similarity and is applied within each age group. Additionally, Support Vector Machine (SVM), a robust classification algorithm, classifies books into specific categories, allowing for personalized recommendations based on age and genre preferences. Decision Tree is introduced, offering transparency in decision-making, ensuring that users understand the rationale behind book recommendations. Naive Bayes, a probabilistic model, significantly enhances the system's efficiency while considering age and genre. Furthermore, the paper highlights key findings, identifies areas for potential enhancement, and suggests promising avenues for future research. In sum, this research makes a substantial contribution to the ever-evolving realm of recommendation systems by focusing on the distinct needs of young readers.