基于多标签分类的智慧图书馆个性化推荐系统

Personalized Recommendation System of Smart Library Based on Multi-label Classification

针对现有图书馆推荐系统存在的推荐准确率低、推荐效率差的问题,提出基于多标签分类的智慧图书馆个性化推荐系统。本研究先对推荐系统整体结构进行分析,阐述了数据库及主要功能模块的设计,然后以此为基础开展软件设计,基于多标签分类算法完成图书馆资源分类,然后采用协同过滤算法完成图书馆资源个性化推荐。最后采用实验证明所提系统的实用性。实验结果表明:所提系统推荐准确率高达95%,且其推荐效率较高,优于对比系统,具有较大的研究价值。

Aiming at the problems of low recommendation accuracy and low recommendation eficiency in existing library recommendation systems,a personalized recommendation system for smart libraries based on multi-label classification is proposed.This research first analyzes the overall structure of the recommendation system,expounds the design of the database and the main functional modules.Then it develops the software design,completes the classification of library resources based on the multi-label classification algorithm,and selects the collaborative filtering algorithm to complete the personalized recommendation of library resources.Finally,experiments are carried out to prove the practicability of the proposed system.The experimental results show that the recommended accuracy of the proposed system is as high as 95%,and its recommendation efficiency is higher than that of the comparison system,which has greater research value.