中文    English

Journal of library and information science in agriculture ›› 2019, Vol. 31 ›› Issue (5): 50-60.doi: 10.13998/j.cnki.issn1002-1248.2019.05.19-0154

• Research paper • Previous Articles     Next Articles

Study on Personalized Book Recommendation Service for New College Readers

WANG Shengbin   

  1. Beihua University,Jilin Jilin 132013, China
  • Received:2019-03-05 Online:2019-05-05 Published:2019-05-05

PDF (PC)

353

Abstract: [Purpose/Significance]Aimed at the user cold start problem that prevent the university smart library from accurately recommending books to new readers, a personalized recommendation method for new college readers is proposed to provide practical solutions for carrying out personalized recommendation service and increasing new readers' borrowing rate.[Method/Process]Through data mining on borrowing circulating big data in Beihua university library, the conclusion is reached that new readers and existing readers with similar attributes have similar borrowing and reading preferences;Then, the singular value decomposition is used to solve the data sparse problem and the Euclidean distance and ant colony algorithm are used to cluster the new readers and existing readers, which building a bridge between new readers and existing readers. Finally, the Top-N algorithm is adopted to recommend the books borrowed by existing readers to new readers. [Result/Conclusion]Take the readers from grade 2017 as experiment subject, the proposed algorithm was tested on 44 readers from three academies. The results show that the proposed algorithm is effective and easy to operate, which lays a foundation for the subsequent personalized recommendation work.

Key words: new readers, personalized recommendation, user cold start, data sparsity, clustering

CLC Number: 

  • G252
[1] 中华人民共和国中央人民政府国务院.2014年政府工作报告[EB/OL].[2019-03-20]. http://www.gov.cn/guowuyuan/2014-03/05/content_2629550.htm.
[2] 中华人民共和国中央人民政府国务院.2015年政府工作报告[EB/OL].[2019-03-20]. http://www.gov.cn/guowuyuan/2015-03/16/content_2835101.htm.
[3] 中华人民共和国中央人民政府国务院.2016年政府工作报告[EB/OL].[2019-03-20]. http://www.gov.cn/guowuyuan/2016-03/05/content_5049372.htm.
[4] 中华人民共和国中央人民政府国务院.2017年政府工作报告[EB/OL].[2019-03-20]. http://www.gov.cn/premier/2017-03/16/content_5177940.htm.
[5] 中华人民共和国中央人民政府国务院.2018年政府工作报告[EB/OL].[2019-03-20]. http://www.gov.cn/zhuanti/2018lh/2018zfgzbg/zfgzbg.htm.
[6] 于良芝,于斌斌.图书馆阅读推广——循证图书馆学(EBL)的典型领域[J].国家图书馆学刊,2014,96(6):9-16.
[7] 王波. 阅读推广、图书馆阅读推广的定义——兼论如何认识和学习图书馆时尚阅读推广案例[J].图书馆论坛,2015,10:1-7.
[8] 许天才,杨新涯,王宁,魏群义.图书馆阅读推广的多元趋势研究——以首届高校图书馆阅读推广大赛为案例[J].图书情报工作,2016,60(2):82-86.
[9] 黄运红,于静.论高校图书馆阅读推广服务的创新——以第二届全国高校图书馆服务创新案例大赛为例[J].图书馆论坛,2018,10:1-7.
[10] JIA R F, JIN M Z, LIU C.A New Clustering Method For Collaborative Filtering[C], 2010 International Conference on Networking and Information Technology,2010,11.
[11] YANG Z H, XU L, CAI Z M.Re-scale AdaBoost for Attack Detection in Collaborative Filtering Recommender Systems[J]. Knowledge-Based Systems,2016,100:74-88.
[12] WANG F H, SHAO H M.Effective personalized recommendation based on time-framed navigation clustering and association mining[J]. Expert Systems with Applications,2004,27(3):365-377.
[13] 鲍玉斌,王大玲,于戈.关联规则和聚类分析在个性化推荐中的应用[J].东北大学学报(自然科学版),2003,24(12):1149-1152.
[14] ZHOU T, REN J, Medo M, etc. Bipartite network projection and personal recommendation[J]. Physical review. E, Statistical, nonlinear, and soft matter physic,2007,76(2):046115.
[15] 张新猛,蒋盛益.基于加权二部图的个性化推荐算法[J].计算机应用,2012,32(3):654-657,678.
[16] CHEN L J, ZHANG Z K, LIU J H, etc.A vertex similarity index for better personalized recommendation[J]. Physica A: Statistical Mechanics and its Applications,2017,466:607-615.
[17] 李克潮,黎晓.个性化图书推荐研究[J].图书馆学研究,2011,20(18):65-69.
[18] 凌霄娥,周兵,李克潮.面向新读者和新图书的数字图书馆个性推荐冷启动问题研究[J].情报理论与实践,2014,38(8):99-104.
[19] 刘庆麟. 基于小数据的图书馆精准服务研究[J].图书馆工作与研究,2017,5,45-50.
[20] 王祥德,雷玉霞,闫昱姝.基于矩阵填充的SVD协同过滤算法研究[J].微型机与应用,2017,36(19):55-61.
[21] 赵烨,黄泽君.蚁群K-medoids融合的聚类算法[J].电子测量与仪器学报,2012,26(9):800-804.
[1] Jia LIU. Innovation and Risk Avoidance of Smart Library Services Based on Generative Artificial Intelligence [J]. Journal of library and information science in agriculture, 2024, 36(7): 63-75.
[2] Yan MOU. Social Media Users' Expression, Formation Mechanism, and Coping Strategies for Algorithm Aversion [J]. Journal of library and information science in agriculture, 2024, 36(10): 86-94.
[3] LI Canyao, WEI Wei, LIU Xiaoli, ZHOU Linxing, WANG Shuai. Multi-modal Characteristics Analysis and Customer Service Efficiency Improvement in the Digital Community Based on User Clustering [J]. Journal of library and information science in agriculture, 2023, 35(2): 30-44.
[4] HUANG Taihua, ZHANG Tao, WANG Lei. Construction of College Students' "Consumption-Academic-Social" Profiles from the Perspective of Multi-source Data Fusion [J]. Journal of library and information science in agriculture, 2022, 34(7): 76-87.
[5] YU Liping, PAN Weibo. Key Indicators of Journal Evaluation Based on K-means and PLS-DA [J]. Journal of library and information science in agriculture, 2022, 34(12): 55-64.
[6] ZHAO Lei, ZHANG Chengzhi. Difference Analysis of Research Topics in a Specific Domain Based on Different Content Levels [J]. Journal of library and information science in agriculture, 2021, 33(5): 14-27.
[7] LI Lei, SONG JianNing, SONG TianHua. Technology Forecasting Based on Topic Identification of Online Innovation Communities and S-Curve [J]. Journal of library and information science in agriculture, 2021, 33(4): 45-57.
[8] YU Li. Discipline Development Trend Analysis based on Text Semantic Understanding [J]. Journal of library and information science in agriculture, 2020, 32(3): 29-36.
[9] LIU Li. Analysis on Agricultural Science and Technology Resources and Dynamic Simulation Research on Resources Utilization [J]. Journal of library and information science in agriculture, 2019, 31(3): 65-74.
[10] ZHENG Minjiao, JIA Bei, LING Yanxiang. Research on Personalized News Recommendation System in Mobile Environment [J]. Journal of library and information science in agriculture, 2019, 31(1): 69-73.
[11] ZHANG Chuanzhen, WANG Huanjing. Research on Domestic Information Resource Management Based on Co-word Cluster Analysis [J]. Journal of library and information science in agriculture, 2018, 30(3): 48-52.
[12] DAI Yanjie, NIU Lili, ZHAO Zifei. Comparison and Analysis of Domestic and Foreign Research Hotspots on Meteorology Based on Double Clustering [J]. Journal of library and information science in agriculture, 2017, 29(5): 80-84.
[13] XIAO Shenhua. Analysis of the hot spots in the subject of agriculture and environmental science [J]. Journal of library and information science in agriculture, 2017, 29(1): 44-48.
[14] ZHANG Wei-dong. Research on Library Management data Clustering and FCM Clustering Algorithm of Multi Dimension Attribute Weights Based on the Optimization [J]. Journal of library and information science in agriculture, 2016, 28(6): 50-57.
[15] WU Ming-zhi, ZHANG Bing. Status of Domestic MOOC Research Based on Biblimetric and Contents Analysis [J]. Journal of library and information science in agriculture, 2016, 28(4): 57-61.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Editorial Board of Journal of library and information science in agriculture
Tel：(010)82109667　Fax：(010)82109667　E-mail：xuekan＠caas.cn
Powered by Beijing Magtech Co., Ltd.
知识共享许可协议