Personalization Method of E-Catalog Based on User Interesting Degree

doi:10.1007/s12204-012-1255-3

Abstract

Abstract: The user interesting degree evaluation index is designed to fulfill the users’ real needs, which includes the user’ attention degree of commodity, hot commodity and preferential commodity. User interesting degree model (UIDM) is constructed to justify the value of user interesting degree; the personalization approach is presented; operations of add and delete nodes (branches) are covered in this paper. The improved e-catalog is more satisfied to users’ needs and wants than the former e-catalog which stands for enterprises, and the improved one can complete the recommendation of related products of enterprises.

Key words:

摘要： The user interesting degree evaluation index is designed to fulfill the users’ real needs, which includes the user’ attention degree of commodity, hot commodity and preferential commodity. User interesting degree model (UIDM) is constructed to justify the value of user interesting degree; the personalization approach is presented; operations of add and delete nodes (branches) are covered in this paper. The improved e-catalog is more satisfied to users’ needs and wants than the former e-catalog which stands for enterprises, and the improved one can complete the recommendation of related products of enterprises.

关键词:

CLC Number:

TP 301

NIE Gui-hua (聂规划), XU Shang-ying (徐尚英), CHEN Dong-lin (陈冬林). Personalization Method of E-Catalog Based on User Interesting Degree[J]. Journal of shanghai Jiaotong University (Science), 2012, 17(2): 215-222.

References

[1] Shim J, Shim S S Y. Ontology-based e-catalog in ecommerce [J]. Electronic Commerce Research and Applications,2006, 5(1): 1-3.

[2] Kim D, Lee S, Chun J, et al. A semantic classification model for e-catalogs [C]//Proceedings of the IEEE International Conference on E-Commerce Technology.

Piscataway, NJ: IEEE, 2004: 85-92.

[3] Amiri A. Customer-oriented catalog segmentation: Effective solution approaches [J]. Decision Support Systems, 2006, 42: 1860-1871.

[4] Blanco-Fern´andez Y, Pazos-Arias J J, Gil-Solla A, et al. A flexible semantic inference methodology to reason about user preferences in knowledgebased

recommender systems [J]. Knowledge-Based Systems, 2008, 21: 305-320.

[5] Fensel D, Ding Y, Omelayenko B, et al. Product data integration in B2B electronic commerce [J]. IEEE Intelligence System, 2001, 16(3): 54-59.

[6] Agrawal R, Srikant R. On integrating catalogs [C]// Proceedings of the Tenth International World Wide Web Conference. Hong Kong: ACM, 2001: 603-612.

[7] Harmonized System Committee. Harmonized system convention [EB/OL]. (2011-11-08). http://www.wcoomd.org/ie/En/Topics Issues/HarmonizedSystem/harmonizedsystem.htm.

[8] UNDP. United Nations standard products and service code [EB/OL]. (2011-11-08). http://www.unspsc.org/2001.

[9] Cologne Institute for Business Research. Ecl@ssnew standardized material and service classification[EB/OL]. (2011-11-08). http://www.eclassonline.com.

[10] Lin C, Hong C. Using customer knowledge in designing electronic catalog [J]. Expert Systems with Applications, 2008, 34: 119-127.

[11] Ji H F, Yang M C, Honda T. A probabilistic approach for extraction design preferences from design team discussion [C]//Proceedings of the ASME 2007

International Design Engineering Technical Conference & Computer and Information in Engineering Conference. Las Vegas, USA: ASME, 2007: 297-306.

[12] Orsborn S, Cagan J, Boatwright P. Quantifying aesthetic form preference in a utility function [J]. Journal of Mechanical Design, 2009, 131(6): 1-10.

[13] Macdonald E F, Gonzalez R, Papalambros P Y. Preference inconsistency in multidisciplinary design decision making [J]. Journal of Mechanical Design,

2009, 131(3): 1-13.

[14] Chen D L, Li X F, Xia H, et al. Technology of electronic catalogs semantic query and system research [C]//Proceedings of 2009 WRI World Congress on

Software Engineering, WCSE 2009. Piscataway, NJ: IEEE, 2009: 146-149.

[15] Zeng Q T, Zhao Z Y, Liang Y Q. Course ontologybased user’s knowledge requirement acquisition from behaviors within e-learning systems [J]. Computers &

Education, 2009, 53: 809-818.

[16] Chen Dong-lin, Zhang Jun, Li Xiao-fei. Intelligent service system of e-catalog based on semantic integration [J]. Journal of Intelligence, 2010, 29(12): 129-133

(in Chinese).

[17] Bellatreche L, Dung N X, Pierra G, et al. Contribution of ontology-based data modeling to automatic integration of electronic catalogues within engineering

databases [J]. Computers in Industry, 2006, 57: 711-724.

[18] Li Xiao-fei. Research on the theory and method of customer oriented e-catalog ontology self-learning [D].Wuhan: School of Economics, Wuhan University of

Technology, 2010 (in Chinese).

[19] Nie Gui-hua, Luo Ji, Chen Dong-lin. Research on SWRL rules of e-catalog [J]. Computer Engineering and Applications, 2011, 47(7): 57-60 (in Chinese).

[20] Chen D L, Li X F, Liang Y L, et al. A semantic query approach to personalized e-catalogs service system [J]. Journal of Theoretical and Applied Electronic

Commerce Research, 2010, 5(3): 39-54.

[21] Kwon I H, Kim C O, Kim K P, et al. Recommendation of e-commerce sites by matching category-based buyer query and product e-catalogs [J]. Computers in

Industry, 2008, 59: 380-394.

[22] Calegari S, Ciucci D. Granular computing applied to ontologies [J]. International Journal of Approximate Reasoning, 2010, 51: 391-409.

[23] Chen D L, Li X F, Liang Y L, et al. Research on the theory of customer-oriented e-catalog ontology automatic construction [C]//Proceedings of 2010 International

Conference on E-Business and E-Government. Piscataway, NJ: IEEE, 2010: 2961-2964.