基于机器学习的个性化图书馆资源推荐系统设计  

Design of a library resource recommendation system based on machine learning

作　　者：艾里亚尔·阿不都克里木[1] 陈英杰 Ailiyaer Abudukelimu;CHEN Yingjie(Library,Xinjiang Agricultural University,Urumqi 830052,China)

机构地区：[1]新疆农业大学图书馆,新疆乌鲁木齐830052

出　　处：《无线互联科技》2024年第23期39-43,共5页Wireless Internet Science and Technology

摘　　要：随着信息技术的发展,图书馆面临资源管理和个性化推荐的挑战。传统推荐方法依赖人工规则或统计模型,难以满足用户的个性化需求。文章提出一种基于机器学习的个性化图书馆资源推荐系统,结合深度学习语言模型(如ChatGPT)对用户需求进行精准建模。通过分析用户行为数据和语义信息,文章设计了一种新的推荐框架,旨在提高推荐系统的智能化水平。实验结果表明,所提出的系统在推荐精度和用户满意度方面均显著优于传统方法,能够为用户提供更加个性化和精准的图书馆资源推荐。该研究为图书馆资源推荐系统的设计与优化提供了理论支持和实践指导。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.

关 键 词：机器学习 图书馆 资源推荐 ChatGPT 用户行为分析 

分 类 号：G258.3[文化科学—图书馆学]