Book recommendation systems are essential in the digital age for assisting users in finding pertinent books among large libraries. In order to provide individualized suggestions, this study suggests a collaborative filtering web-based book recommendation system that combines item-based and user-based techniques. In order to increase accuracy, the system uses machine learning approaches to gather user preferences through implicit actions and explicit evaluations. Common difficulties including data sparsity, cold-start concerns, and scalability problems are also covered in the study. The system is trained and evaluated using real-world library borrowing records and a Kaggle dataset. The approach's efficacy is demonstrated by a number of performance indicators, including accuracy, recall, and F-measure, with an enhanced F-measure of 80.38%. Comparative analysis suggests that hybrid filtering techniques combining collaborative filtering with content-based methods further enhance recommendation accuracy. The proposed system is designed to be deployed in libraries and online book platforms, improving user satisfaction and engagement.
Kaggle dataset
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