Comparison of Multiple Book Recommendation Algorithms After Analysis of User Characteristics Using Big Data

IF 0.5 Q3 INFORMATION SCIENCE & LIBRARY SCIENCE

Technical Services Quarterly Pub Date : 2023-11-21 DOI:10.1080/07317131.2023.2259691

Ruixia Wang, Na He

引用次数: 0

Abstract

Through big data analysis of borrowing information, it is possible to find the characteristics of different users. This paper applied K-means clustering to classify users. User feature vectors were...

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基于大数据分析用户特征的多种图书推荐算法比较

通过对借阅信息的大数据分析，可以发现不同用户的特点。本文采用K-means聚类对用户进行分类。用户特征向量是…

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Technical Services Quarterly INFORMATION SCIENCE & LIBRARY SCIENCE-

CiteScore

0.80

自引率

0.00%

发文量

期刊介绍： Technical Services Quarterly is dedicated to providing a forum for the presentation of current developments and future trends concerning the technical operations of libraries and information centers. The journal aims to keep its readers informed of current developments and future trends in research, developments, and practical implementation of systems and applications of traditional and non-traditional technical services and the public operations they influence and sustain. The journal accepts original research, theoretical, and implementation articles pertaining to technical services, automation, networking, document delivery, information technology, library instruction and information literacy, reference and bibliography, case studies, cost analysis, staffing, etc.