基于大数据的智慧图书馆图书推荐算法研究  

Research on Book Recommendation Algorithm of Smart Libraries Based on Big Data

作　　者：何宏达 He Hongda(Tianjin Hexi District Library,Tianjin 300210,China)

机构地区：[1]天津市河西区图书馆,天津300210

出　　处：《图书情报研究》2025年第1期117-123,共7页Library and Information Studies

摘　　要：[目的/意义]为提高大数据时代下智慧图书馆管理效率及图书推荐准确度,在研究数据挖掘、特征提取、用户兴趣模型的基础上,设计了一种智能图书馆服务平台。[方法/过程]根据图书信息、用户信息和阅读信息,采用大数据分布式处理技术对数据进行识别和挖掘,实现基于用户兴趣的阅读推荐。在仿真环节,通过比较不同方法的推荐准确率和时间复杂度,将所提方法与VSM、LSI、CART进行了比较与分析。[结果/结论]结果表明,所提方法推荐结果更符合用户不同推荐个数需求,且随着推荐个数增加,所提方法性能提升越明显。当用户需求推荐个数为8时,所提方法满意度可达56.13%,高于LSI方法的51.98%和CART方法的52.03%;VSM方法满意度最低,仅为41.25%。时间复杂度对比结果表明,当用户推荐图书数量为4时,所提方法时间复杂度为1.95s,较VSM方法相比减少47.1%。实验结果验证了所提方法的有效性和适用性,可为智慧图书馆服务质量提升提供一定借鉴作用,具有广泛的应用前景。[Purpose/significance]in In order to improve the management efficiency of Intelligent Library and the accuracy of book recommendation in the era of big data,an intelligent library service platform is designed based on the research of data mining,feature extraction and user interest model.[Method/process]according According to the book information,user information and reading information,the big data distributed processing technology is used to identify and mine the data,so as to realize the reading recommendation based on user interest.In the simulation part,by comparing the accuracy and time complexity of different methods,the proposed method is compared and analyzed with VSM,LSI and cart.[Result/conclusion]The results show that the recommendation results of the proposed method are more in line with the needs of users with different number of recommendations,and the performance improvement of the proposed method is more obvious with the increase of the number of recommendations.When the number of recommendations is 8,the satisfaction of the proposed method is 56.13%,which is higher than 51.98%of LSI method and 52.03%of cart method;The satisfaction of VSM method was is the lowest,only 41.25%.The time complexity comparison results show that when the number of books recommended by users is 4,the time complexity of the proposed method is 1.95s,which is 47.1%less than that of VSM method.The experimental results verify the ef fectiveness and applicability of the proposed method,which can provide a reference for improving the service quality of intelligent library,and has a broad application prospect.

关 键 词：智慧图书馆 数据挖掘 智能推荐 特征提取 奇异值分解 基于内容的推荐 协同过滤 

分 类 号：G252[文化科学—图书馆学] TP393[自动化与计算机技术—计算机应用技术]