Please wait a minute...
New Technology of Library and Information Service  2013, Vol. Issue (5): 46-53    DOI: 10.11925/infotech.1003-3513.2013.05.06
Current Issue | Archive | Adv Search |
A Visualization and Recognition Method of Readers’ Interests with the Analysis of the Characteristics of Borrowing Time
Li Shuqing1, Wang Jianqiang2
1. College of Information Engineering, Nanjing University of Finance & Economics, Nanjing 210046, China;
2. Department of Library and Information Studies, Graduate School of Education, University at Buffalo, The State University of New York, Buffalo 14260, USA
Export: BibTeX | EndNote (RIS)      
Abstract  The recognition of the characteristics of users’ personalized interests can be enhanced by utilizing of the information in the users’ accessing time. This paper proposes a method of constructing readers’ personalized profiles with the timing information of readers’ borrowing records in book recommendation service of library. This paper begins with the introduction of three extended time indexes based on the analysis of the characteristics of readers’ borrowing time, meantime, it also discusses the recognition of the degree of readers’ interests, and the visualization of timing evolution trend of readers’ interests. Finally, some related experiments that show the performance improvements are reported.
Key wordsPersonalization      Time analysis      Visualization      Book recommendation service     
Received: 12 April 2013      Published: 03 July 2013
:  G202  

Cite this article:

Li Shuqing, Wang Jianqiang. A Visualization and Recognition Method of Readers’ Interests with the Analysis of the Characteristics of Borrowing Time. New Technology of Library and Information Service, 2013, (5): 46-53.

URL:     OR

[1] McLaughlin J E.Personalization in Library Databases: Not Persuasive Enough?[J].Library Hi Tech,2011,29(4):605- 622.
[2] Zandian F, Riahinia N, Azimi A, et al. An Evaluation of Alert Services: Quantity Versus Quality[J]. Program: Electronic Library and Information Systems, 2010, 44(1): 5-12.
[3] Lee T Q,Park Y, Park Y T.An Empirical Study on Effectiveness of Temporal Information as Implicit Ratings[J]. Expert Systems with Applications, 2009, 36(2): 1315-1321.
[4] Liu D R, Shih Y Y.Hybrid Approaches to Product Recommendation Based on Customer Life Time and Purchase Preferences[J]. Journal of Systems and Software, 2005, 77(2): 181-191.
[5] Ding Y, Li X, Orlowska M E. Recency-based Collaborative Filtering[C]. In: Proceedings of the 17th Australasian Database Conference. 2006: 99-107.
[6] Yu J, Gong J, Liu F F. Building Search Context with Sliding Window for Information Seeking[C]. In: Proceedings of the 3rd International Conference on Computer Research and Development. 2011: 274-277.
[7] Yu J, Liu F F, Zhao H H. Building User Profile Based on Concept and Relation for Web Personalized Services[C]. In: Proceedings of the International Conference on Innovation and Information Management. 2012:165-172.
[8] Abel F, Gao Q, Houben G J, et al. Analyzing Temporal Dynamics in Twitter Profiles for Personalized Recommendations in the Social Web[C]. In: Proceedings of the 3rd International Conference on Web Science, Koblenz, Germany. 2011: 1-8.
[9] Abraham S, Sojan Lal P, Georgeet D. WEBTRACLUS: A Spatio-Temporal Trajectory Clustering Tool for Personalization in Healthcare Web Portals[C]. In: Proceedings of the 4th International Conference on Pervasive Technologies Related to Assistive Environments(PETRA ’11). New York, NY, USA: ACM, 2011:65-66.
[10] White R W, Bennett P N, Dumais S T. Predicting Short-term Interests Using Activity-based Search Context[C]. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management. 2010: 1009-1018.
[11] Sontag D, Collins-Thompson K, Bennett P N, et al. Probabilistic Models for Personalizing Web Search[C]. In: Proceedings of the 5th ACM International Conference on Web Search and Data Mining. 2012: 433-442.
[12] Bennett P N, White R W, Chu W, et al. Modeling the Impact of Short-and Long-term Behavior on Search Personalization[C]. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2012: 185-194.
[13] Yang D, Nie T Z, Shen D R, et al. Personalized Web Search with User Geographic and Temporal Preferences[C]. In: Proceedings of the 13th Asia-Pacific Web Conference on Web Technologies and Applications. 2011: 95-106.
[14] Boughareb D, Farah N. Toward a Web Search Personalization Approach Based on Temporal Context[C]. In: Proceedings of Communications in Computer and Information Science. 2011: 33-44.
[15] Dhanalakshmi D, Kousalya R, Saravanan V. Time Based Web User Personalization and Search[J]. International Journal of Computer Applications, 2012, 46(23): 11-17.
[16] Ho S Y, Bodoff D, Tam K Y. Timing of Adaptive Web Personalization and Its Effects on Online Consumer Behavior[J]. Information Systems Research, 2011,22(3): 660-679.
[17] 胡蓓蓓. 基于知识决策的数字图书馆个性化推荐[J]. 情报学报 , 2007, 26(3): 448-455.(Hu Beibei. Personalized Recommendation in Digital Library Based on Knowledge Decision-making[J].Journal of the China Society for Scientific and Technical Information, 2007,26(3):448-455.)
[18] 景民昌, 于迎辉. 基于借阅时间评分的协同图书推荐模型与应用[J]. 图书情报工作 ,2012,56(3):117-120.(Jing Minchang, Yu Yinghui. CF Recommending Model Based on Borrowing-time Scores and Its Application[J]. Library and Information Service, 2012,56(3): 117-120.)
[19] 马健, 杜泽宇, 李树青, 等.基于多兴趣特征分析的图书馆个性化图书推荐方法[J]. 现代图书情报技术 , 2012(6):1-8. (Ma Jian, Du Zeyu, Li Shuqing. Personalized Book Recommendation Algorithm Based on Multi-interest Analysis in Library[J]. New Technology of Library and Information Service,2012(6): 1-8.)
[20] 张炜, 李斌.基于联机公共查询目录的读者行为挖掘的个性化智能服务系统构建[J]. 情报理论与实践 , 2009,32(10):68-71. (Zhang Wei, Li Bin. Construction of the Individual Intelligent Service System Based on the Mining of Readers’ Behavior in OPAC Database[J]. Information Studies: Theory & Application,2009,32(10):68-71.)
[21] 李克潮, 梁正友. 基于多特征的个性化图书推荐算法[J]. 计算机工程 , 2012,38(11): 34-37.(Li Kechao, Liang Zhengyou. Personalized Book Recommendation Algorithm Based on Multi-feature [J].Computer Engineering,2012,38(11): 34-37.)
[22] 陈春颖, 熊拥军. 基于序列模式挖掘的读者借阅行为分析[J]. 图书情报知识 , 2011(4): 92-96.(Chen Chunying, Xiong Yongjun. The Analysis of Reader Borrow Behavior Based on Sequential Pattern Mining[J]. Document, Information & Knowledge, 2011(4): 92-96.)
[1] Chen Ting,Wang Haiming,Wang Xiaomei. Detecting Funding Topics Evolutions with Visualization[J]. 数据分析与知识发现, 2020, 4(2/3): 60-67.
[2] Haici Yang,Jun Wang. Visualizing Knowledge Graph of Academic Inheritance in Song Dynasty[J]. 数据分析与知识发现, 2019, 3(6): 109-116.
[3] Yanan Yang,Wenhui Zhao,Jian Zhang,Shen Tan,Beibei Zhang. Visualizing Policy Texts Based on Multi-View Collaboration[J]. 数据分析与知识发现, 2019, 3(6): 30-41.
[4] Jiang Wu,Guanjun Liu,Xian Hu. An Overview of Online Medical and Health Research: Hot Topics, Theme Evolution and Research Content[J]. 数据分析与知识发现, 2019, 3(4): 2-12.
[5] Zhiqiang Wu,Zhongming Zhu,Wei Liu,Sili Wang. Research and Practice on the Extension of Knowledge Analysis and Visualization Function in CSpace[J]. 数据分析与知识发现, 2019, 3(3): 112-119.
[6] Chen Ting,Li Guopeng,Wang Xiaomei. Visualizing Appropriation of Research Funding with t-SNE Algorithm[J]. 数据分析与知识发现, 2018, 2(8): 1-9.
[7] Yang Sinan,Xu Jian,Ye Pingping. Review of Online Sentiment Visualization Techniques[J]. 数据分析与知识发现, 2018, 2(5): 77-87.
[8] Wang Li,Zou Lixue,Liu Xiwen. Visualizing Document Correlation Based on LDA Model[J]. 数据分析与知识发现, 2018, 2(3): 98-106.
[9] Xie Xiufang,Zhang Xiaolin. Integrated Analysis and Visualization of Sci-Tech Roadmaps: Case Study of Renewable Energy[J]. 数据分析与知识发现, 2017, 1(1): 16-25.
[10] Luo Wenxin,Chen Chong,Deng Siyi. Detecting Disease Associations with Word2Vec from Consumer Health Information[J]. 现代图书情报技术, 2016, 32(9): 78-87.
[11] Chen Ting,Wang Xiaomei,Lv Weimin. ng-info-chart: The Visualization Component Based on Customized HTML Tags[J]. 现代图书情报技术, 2016, 32(6): 88-95.
[12] Li Jinhua,An Zhongjie. Analyzing Geographical Coordinates Data for Micro-blog Trending Events[J]. 现代图书情报技术, 2016, 32(2): 90-101.
[13] Lixin Xia,Ying Tan. Analysis and Visualization of the LOD Network Structure[J]. 现代图书情报技术, 2016, 32(1): 65-72.
[14] Peng Hao, Xu Jian, Xiao Zhuo. Sentiment Analysis of Web Reviews Based on Comparative Sentence Extraction[J]. 现代图书情报技术, 2015, 31(12): 48-56.
[15] Zheng Yangyang, Xu Jian, Xiao Zhuo. Utilization of Sentiment Analysis and Visualization in Online Video Bullet-screen Comments[J]. 现代图书情报技术, 2015, 31(11): 82-90.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938