Hybrid Recommendation Framework for Personalized Library Services Based on Deep Learning

The swift digitalization of libraries has presented novel opportunities and challenges in the management and accessibility of extensive digital resources. Personalized recommendation systems have become an essential tool for improving user experience by effectively linking consumers with pertinent information. Nonetheless, conventional methods, such collaborative filtering and content-based filtering, frequently encounter challenges related to data sparsity, cold start problems, and scalability in practical library applications. This study presents a hybrid recommendation architecture that integrates collaborative filtering, content-based filtering, and deep neural networks to tackle these difficulties. The system utilizes feature embedding methods to acquire dense representations of users and items, so efficiently addressing data sparsity. A transfer learning method is employed to enhance cold start performance by applying pre-trained knowledge to novel user and object contexts. Experimental assessments on an actual library dataset reveal substantial enhancements in suggestion precision and ranking efficacy compared to conventional and cutting-edge techniques. The findings underscore the pivotal function of the deep learning module in identifying non-linear correlations between user preferences and resource features, hence facilitating robust and scalable suggestions. This study provides a thorough solution to ongoing issues in library recommendation systems, facilitating improved user satisfaction and effective resource discovery in digital libraries.