Design of a library resource recommendation system based on machine learning
With the development of information technology,libraries are facing challenges in resource management and personalized recommendation.Traditional recommendation methods,relying on manual rules or statistical models,struggle to meet users'growing need for personalization.This paper proposes a personalized library resource recommendation system based on machine learning,leveraging deep learning language models(such as ChatGPT)to accurately model user needs.By analyzing user behavior data and semantic information,a new recommendation framework is designed to enhance the intelligence of the recommendation system.Experimental results show that the proposed system significantly outperforms traditional methods in terms of recommendation accuracy and user satisfaction,providing more personalized and precise library resource recommendations.This study offers theoretical support and practical guidance for the design and optimization of library resource recommendation systems.
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