Research on Personalized Recommendation of University Librar

发布时间：2023-04-22 15:24

　　随着信息技术与互联网的飞速发展,高校读者仅靠传统的基于检索的服务很难从海量图书中发现真正感兴趣的或对其有价值的图书。应用数据挖掘技术和个性化推荐技术,根据读者自身信息需求的差异,将符合读者需求的图书主动推荐给读者,这种主动服务方式不仅提高了高校图书馆的服务水平,使高校图书馆发展地更加全面、人性化,还可以发掘读者潜在的信息需求,提高馆藏图书的借阅率,将图书资源利用率最大化。论文旨在研究基于聚类算法的高校图书馆个性化推荐的一般方法,利用常见的几类推荐算法,设计适用于高校图书馆的个性化推荐策略,并将华中师范大学2017年全年图书馆图书借阅信息及2014级-2017级本科生个人信息作为实例数据,进行数据挖掘,从而验证前面设计的个性化推荐策略的可行性。论文首先建立读者-类目偏好模型,对目标读者基于这一偏好模型进行聚类分析。在聚类分析的过程中,首先将原始的k-means聚类算法优化,再使用改进后的K-Means算法实现读者的聚类。接下来,将协同过滤推荐算法与基于内容的推荐算法结合,起到同时实现两个推荐算法的优点的目的。利用协同过滤和基于内容的混合推荐(基于兴趣的推荐)策略,针对每个读者进行图书的个...

【文章页数】：112 页

【学位级别】：硕士

【文章目录】：
Abstract
摘要
Acknowledgements
Chapter 1 Introduction
    1.1 Research Background and Significance
        1.1.1 Research Problem
        1.1.2 Research Significance
    1.2 Related Work
    1.3 Research Content and Method
        1.3.1 Research Content
        1.3.2 Research Method
    1.4 Thesis Structure
Chapter 2 Theoretical Basis
    2.1 Theoretical Basis of Cluster Analysis
        2.1.1 Concept of Reader Segmentation
        2.1.2 Introduction of Data Mining
        2.1.3 Cluster Analysis
        2.1.4 Basic K-Means Algorithm
    2.2 Theoretical Basis of Personalized Recommendation in University Library
        2.2.1 Overview of Recommendation System
        2.2.2 Recommendation Method Used in University Library
        2.2.3 Comparison of 3 Recommendation Algorithms
    2.3 Introduction of Chinese Library Classification
    2.4 Summary
Chapter 3 Related Process of Personalized Recommendation for University Library
    3.1 Optimization Methods on Basic K-Means Algorithm
        3.1.1 Description of Optimized K-Means Algorithm
        3.1.2 Algorithm Characteristics
        3.1.3 Performance Evaluation of Optimized K-Means Algorithm
    3.2 Design the Framework of Personalized Recommendation in University Li-braries
        3.2.1 "My Library" in CCNU
        3.2.2 Demand Analysis of Personalized Recommendation System in CCNULibrary
        3.2.3 Framework of Personalized Recommendation in CCNU Library
    3.3 Reader Preference Model Based on Classification Number in CLC
    3.4 Algorithm and Strategy of Personalized Recommendation in University Li-brary
        3.4.1 Introduction of Collaborative Filtering Recommendation Algorithm
        3.4.2 Introduction of Content-Based Recommendation Algorithm
        3.4.3 Introduction of Collaborative Filtering and Content-Based CombinedRecommendation Strategy
    3.5 Summary
Chapter 4 Experimental Settings and Results Analysis
    4.1 Data Collection and Preprocessing
        4.1.1 Selection of Data Sources
        4.1.2 Data Cleaning
        4.1.3 Data Conversion
        4.1.4 Data Integration
    4.2 Cluster Algorithm on Readers
        4.2.1 Simulation Experiment on Partial Readers
        4.2.2 Experiment on All Readers
    4.3 Personalized Recommendation on Readers
        4.3.1 Personalized Recommendation Booklist Based on Readers' Interests
        4.3.2 Personalized Recommendation Booklist Based on Faculty Ranking
    4.4 Results Analysis and Evaluation
        4.4.1 Evaluation Indicators for Personalized Recommendation
        4.4.2 Evaluation of Personalized Recommendation of CCNU Library
    4.5 Summary
Chapter 5 Conclusion and Future Work
    5.1 Conclusion
    5.2 Future Work
References



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