Please wait a minute...
New Technology of Library and Information Service  2012, Vol. Issue (12): 72-78    DOI: 10.11925/infotech.1003-3513.2012.12.13
Current Issue | Archive | Adv Search |
Research on Ontology-based Personalized Recommendation Method for Library Resources
Wang Yingzi
Changzhou University Library, Changzhou 213164, China
Download: PDF(940 KB)   HTML  
Export: BibTeX | EndNote (RIS)      
Abstract  The huge increase of library resources makes users’ cost of accessing valuable knowledge becoming much higher. For this problem, the paper proposes a hybrid recommendation method for library resources, which adopts semantic technologies to describe library resources and borrowers, establishes the association between user preferences and library resource features. Through query modification, rule-based and case-based inference, the method realizes personalized recommendation. Meanwhile, some auxiliary recommendation approaches are integrated. The recommendation process can be analyzed and optimized according to users’ feedback. Additionally, this method reduces the “new user” and “new item” problems in traditional collaborative filtering method to a certain extent. Experimental results show that the proposed method can enhance the hit rate.
Key wordsOntology      Hybrid recommendation      Personalized recommendation      User preference     
Received: 12 October 2012      Published: 12 March 2013
:  TP391  

Cite this article:

Wang Yingzi. Research on Ontology-based Personalized Recommendation Method for Library Resources. New Technology of Library and Information Service, 2012, (12): 72-78.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2012.12.13     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2012/V/I12/72

[1] Crespo R G, Martinez O S, Lovelle J M C, et al. Recommendation System Based on User Interaction Data Applied to Intelligent Electronic Books[J]. Computers in Human Behavior, 2011, 27(4): 1445-1449.
[2] Adomavicius G, Tuzhilin A. Towards the Next Generation of Recommender Systems: A Survey of the State-of Art and Possible Extensions[J]. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(6): 734-749.
[3] 赵继海. 论数字图书馆个性化定制服务[J]. 中国图书馆学报, 2001, 27(3): 63-65.(Zhao Jihai. On Personalized Customization Services of Digital Library[J].Journal of Library Science in China, 2001, 27 (3): 63-65.)
[4] Gruber T R. A Translation Approach to Portable Ontology Specifications[J]. Knowledge Acquisition, 1993, 5(2): 199-220.
[5] Rho S, Song S, Hwang E, et al. COMUS: Ontological and Rule-based Reasoning for Music Recommendation System[C]. In: Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD’09). Heidelberg,Berlin: Springer-Verlag,2009: 859-866.
[6] Yang S Y. Developing an Ontology-supported Information Integration and Recommendation System for Scholars[J]. Expert Systems with Applications, 2010, 37(10): 7065-7079.
[7] Chen R C, Huang Y H, Bau C T, et al. A Recommendation System Based on Domain Ontology and SWRL for Anti-diabetic Drugs Selection[J]. Expert Systems with Applications, 2012, 39(4): 3995-4006.
[8] 周若静. 本体的构建及其在图书信息检索中的应用研究[D]. 大连: 大连海事大学, 2009. (Zhou Ruojing. Research and Application on Ontology Modeling and Ontology-based Book Information Retrieval[D]. Dalian: Dalian Maritime University, 2009.)
[9] 牟冬梅. 数字图书馆知识组织语义互联策略及其应用研究[D]. 长春: 吉林大学, 2009.(Mu Dongmei. Study on Semantic Interconnection Strategy and Application on Digital Library Knowledge Organization[D]. Changchun: Jilin University, 2009.)
[10] Yan D W, Cen Y H, Zhang W, et al. Ontology-based Framework for Personalized Recommendation in Digital Libraries[J]. Journal of Southeast University:English Edition, 2006, 22(3): 385-388.
[11] 袁静. 基于本体的数字图书馆个性化服务研究[J]. 图书馆建设, 2009 (1): 66-69.(Yuan Jing. Research on the Personalized Service of the Digital Library Based on Ontology[J]. Library Development, 2009(1):66-69.)
[12] 丁雪, 张玉峰. 基于本体的智能数字图书馆个性化推荐用户本体研究[J]. 现代情报, 2009 (12): 61-65.(Ding Xue, Zhang Yufeng. Research on User Ontology in Personalized Recommendation of Ontology-based Intelligent Digital Library[J]. Journal of Modern Information, 2009 (12):61-65.)
[13] 《中国图书馆分类法》编委会.中国图书馆分类法第五版简表[EB/OL].[2012-09-08].http://clc.nlc.gov.cn/ztfdsb.jsp.(Editorial Board of Chinese Library Classification. The Fifth Edition of Summary Table of Chinese Library Classification[EB/OL].[2012-09-08]. http://clc.nlc.gov.cn/ztfdsb.jsp.)
[14] Smith M K, Welty C, McGuinness D L. OWL Web Ontology Language Guide[EB/OL].[2012-09-10]. http://www.w3.org/TR/owl-guide/.
[15] Prud’hommeaux E, Seaborne A. SPARQL Query Language for RDF[EB/OL].[2012-09-15]. http://www.w3.org/TR/rdf-sparql-query/.
[16] The Apache Software Foundation. What is Jena?[EB/OL].[2012-09-15]. http://jena.apache.org/about_jena/about.html.
[17] Wikipedia. Cosine Similarity[EB/OL].[2012-09-17]. http://en.wikipedia.org/wiki/Cosine_similarity.
[18] Likert R. A Technique for The Measurement of Attitudes[J]. Archives of Psychology, 1932, 22(140): 1-55.
[1] Shiqi Deng,Liang Hong. Constructing Domain Ontology for Intelligent Applications: Case Study of Anti Tele-Fraud[J]. 数据分析与知识发现, 2019, 3(7): 73-84.
[2] Zhu Fu,Yuefen Wang,Xuhui Ding. Semantic Representation of Design Process Knowledge Reuse[J]. 数据分析与知识发现, 2019, 3(6): 21-29.
[3] Yiwen Zhang,Chenkun Zhang,Anju Yang,Chengrui Ji,Lihua Yue. A Conditional Walk Quadripartite Graph Based Personalized Recommendation Algorithm[J]. 数据分析与知识发现, 2019, 3(4): 117-125.
[4] Guangshang Gao. A Survey of User Profiles Methods[J]. 数据分析与知识发现, 2019, 3(3): 25-35.
[5] Jiaxin Ye,Huixiang Xiong. Recommending Personalized Contents from Cross-Domain Resources Based on Tags[J]. 数据分析与知识发现, 2019, 3(2): 21-32.
[6] Ying Wang,Li Qian,Jing Xie,Zhijun Chang,Beibei Kong. Building Knowledge Graph with Sci-Tech Big Data[J]. 数据分析与知识发现, 2019, 3(1): 15-26.
[7] Youshi He,Shufang He. Sentiment Mining of Online Product Reviews Based on Domain Ontology[J]. 数据分析与知识发现, 2018, 2(8): 60-68.
[8] Jie Li,Fang Yang,Chenxi Xu. A Personalized Recommendation Algorithm with Temporal Dynamics and Sequential Patterns[J]. 数据分析与知识发现, 2018, 2(7): 72-80.
[9] Huihui Tang,Hao Wang,Zixuan Zhang,Xueying Wang. Extracting Names of Historical Events Based on Chinese Character Tags[J]. 数据分析与知识发现, 2018, 2(7): 89-100.
[10] Beibei Pang,Juanqiong Gou,Wenxin Mu. Extracting Topics and Their Relationship from College Student Mentoring[J]. 数据分析与知识发现, 2018, 2(6): 92-101.
[11] Shengchun Ding,Menglu Liu,Zhu Fu. Unified Multidimensional Model Based on Knowledge Flow in Conceptual Design[J]. 数据分析与知识发现, 2018, 2(2): 11-19.
[12] Haili Tu,Xiaobo Tang. Building Product Recommendation Model Based on Tags[J]. 数据分析与知识发现, 2017, 1(9): 28-39.
[13] Yinxiu Hou,Weiqing Li,Weijun Wang,Tingting Zhang. Personalized Book Recommendation Based on User Preferences and Commodity Features[J]. 数据分析与知识发现, 2017, 1(8): 9-17.
[14] Erjing Chen,Enbo Jiang. Review of Studies on Text Similarity Measures[J]. 数据分析与知识发现, 2017, 1(6): 1-11.
[15] Rujiang Bai,Fuhai Leng,Junhua Liao. An Improved Cosine Text Similarity Computing Method Based on Semantic Chunk Feature[J]. 数据分析与知识发现, 2017, 1(6): 56-64.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn