A web-based personalized business partner recommendation system using fuzzy semantic techniques

Computational Intelligence

Volumn 29, Issue 1, 2013, Pages 37-69

  Lu, Jie ^a   Shambour, Qusai ^a   Xu, Yisi ^a   Lin, Qing ^a   Zhang, Guangquan ^a  

^a UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

Author keywords

business partner; collaborative filtering; e business; fuzzy linguistic; fuzzy sets; recommender systems; semantic relevance; web personalization

Indexed keywords

BUSINESS PARTNERS; COLLABORATIVE FILTERING; EBUSINESS; FUZZY LINGUISTICS; SEMANTIC RELEVANCE; WEB PERSONALIZATION;

ELECTRONIC COMMERCE; FUZZY SETS; RECOMMENDER SYSTEMS;

SEMANTICS;

EID: 84873475006     PISSN: 08247935     EISSN: 14678640     Source Type: Journal    
DOI: 10.1111/j.1467-8640.2012.00427.x     Document Type: Article

References (65)

1. Adomavicius, G., and, A. Tuzhilin,. 2005a. Personalization technologies: A process-oriented perspective. Communications of the ACM, 48: 83-90.
2. Adomavicius, G., and, A. Tuzhilin,. 2005b. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17 (6): 734-749.
3. Albadvi, A., and, M. Shahbazi,. 2009. A Hybrid recommendation technique based on product category attributes. Expert Systems with Applications, 36 (9): 11480-11488.
4. Bivainis, J., 2006. Development of business partner selection. Economics, 73 (1): 7-18.
5. Bollen, J., M. L. Nelson, G. Geisler, and, R. Araujo,. 2007. Usage derived recommendations for a video digital library. Journal of Network and Computer Applications, 30 (3): 1059-1083.
6. Burke, R., 2007. Hybrid web recommender systems. In The Adaptive Web: Methods and Strategies of Web Personalization. Edited by, P. Brusilovsky, A. Kobsa, and, W. Nejdl,. Springer-Verlag: Heidelberg, pp. 377-408.
7. Candillier, L., F. Meyer, and, F. Fessant,. 2008. Designing specific weighted similarity measures to improve collaborative filtering systems. In Advances in Data Mining. Medical Applications, E-Commerce, Marketing, and Theoretical Aspects. Edited by, P. Perner,. Springer: Heidelberg, pp. 242-255.
8. Cao, Y., and, Y. Li,. 2007. An intelligent fuzzy-based recommendation system for consumer electronic products. Expert Systems with Applications, 33 (1): 230-240.
9. Chen, C.-M., and, L.-J. Duh,. 2008. Personalized web-based tutoring system based on fuzzy item response theory. Expert Systems with Applications, 34 (4): 2298-2315.
10. Cho, Y., and, J. Kim,. 2004. Application of web usage mining and product taxonomy to collaborative recommendations in e-commerce. Expert Systems with Applications, 26 (2): 233-246.
11. Cornelis, C., J. Lu, X. Guo, and, G. Zhang,. 2007. One-and-only item recommendation with fuzzy logic techniques. Information Sciences, 177 (22): 4906-4921.
12. Deshpande, M., and, G. Karypis,. 2004. Item-based top-n recommendation algorithms. ACM Transactions on Information Systems, 22 (1): 143-177.
13. Dumais, S. T., G. W. Furnas, T. K. Landauer, S. Deerwester, and, R. Harshman,. 1988. Using latent semantic analysis to improve access to textual information. In Proceedings of the CHI'88 Conference, New York, NY, pp. 281-285.
14. Garfinkel, R., R. Gopal, B. Pathak, and, F. Yin,. 2008. Shopbot 2.0: Integrating recommendations and promotions with comparison shopping. Decision Support Systems, 46 (1): 61-69.
15. Garfinkel, R., R. Gopal, A. Tripathi, and, F. Yin,. 2006. Design of a shopbot and recommender system for bundle purchases. Decision Support Systems, 42 (3): 1974-1986.
16. Gong, S., 2008. The collaborative filtering recommendation based on similar-priority and fuzzy clustering. In Workshop on Power Electronics and Intelligent Transportation System (PEITS 2008), California, IEEE, pp. 248-251.
17. Good, N., J. B. Schafer, J. Konstan, A. Borchers, B. Sarwar, J. Herlocker, and, J. Riedl,. 1999. Combining collaborative filtering with personal agents for better recommendations. In Proceedings of the Sixteenth National Conference on Artificial Intelligence and the Eleventh Conference on Innovative Applications of Artificial Intelligence, American Association for Artificial Intelligence, Orlando, FL, pp. 439-446.
18. Guo, X., and, J. Lu,. 2007. Intelligent e-government services with personalized recommendation techniques. International Journal of Intelligent Systems, 22 (5): 401-417.
19. Herlocker, J., J. A. Konstan, and, J. Riedl,. 2002. An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Information Retrieval, 5 (4): 287-310.
20. Hung, L., 2005. A personalized recommendation system based on product taxonomy for one-to-one marketing online. Expert Systems with Applications, 29 (2): 383-392.
21. IJntema, W., F. Goossen, F. Frasincar, and, F. Hogenboom,. 2010. Ontology-based news recommendation. In Proceedings of the 2010 EDBT/ICDT Workshops, Lausanne, Switzerland, pp. 1-6.
22. Jiang, J. J., and, D. W. Conrath,. 1997. Semantic similarity based on corpus statistics and lexical taxonomy. In Proceedings of the International Conference on Research on Computational Linguistics, Taiwan, pp. 19-33.
23. Jiang, Y., J. Shang, and, Y. Liu,. 2009. Maximizing customer satisfaction through an online recommendation system: A novel associative classification model. Decision Support Systems, 48 (3): 470-479.
24. Jung, J. J., 2011. Service chain-based business alliance formation in service-oriented architecture. Expert Systems with Applications, 38 (3): 2206-2211.
25. Kanellopoulos, D. N., 2008. An ontology-based system for intelligent matching of travellers' needs for group package tours. International Journal of Digital Culture and Electronic Tourism, 1 (1): 76-99.
26. Kim, H.-N., A.-T. Ji, I. Ha, and, G.-S. Jo,. 2010. Collaborative filtering based on collaborative tagging for enhancing the quality of recommendation. Electronic Commerce Research and Applications, 9 (1): 73-83.
27. Korfhage, R. R., 1997. Information Storage and Retrieval. John Wiley & Sons: New York.
28. Kritikou, Y., P. Demestichas, E. Adamopoulou, K. Demestichas, M. Theologou, and, M. Paradia,. 2008. User profile modeling in the context of web-based learning management systems. Journal of Network and Computer Applications, 31 (4): 603-627.
29. Krõl, D., M. Szymanski, and, B. Trawinski,. 2006. The recommendation mechanism in an internet information system with time impact coefficient. International Journal of Computer Science & Applications, 3 (3): 65-80.
30. Lawrence, R. D., G. S. Almasi, V. Kotlyar, M. S. Viveros, and, S. S. Duri,. 2001. Personalization of supermarket product recommendations. Data Mining and Knowledge Discovery, 5 (1-2): 11-32.
31. Lesani, M., and, N. Montazeri,. 2009. Fuzzy trust aggregation and personalized trust inference in virtual social networks. Computational Intelligence, 25 (2): 51-83.
32. Leung, C., S. Chan, and, F. Chung,. 2006. A collaborative filtering framework based on fuzzy association rules and multiple-level similarity. Knowledge and Information Systems, 10 (3): 357-381.
33. Li, Q., J. Zhou, Q.-R. Peng, C.-Q. Li, C. Wang, J. Wu, and, B.-E. Shao,. 2010. Business processes oriented heterogeneous systems integration platform for networked enterprises. Computers in Industry, 61 (2): 127-144.
34. Liang, T.-P., Y.-F. Yang, D.-N. Chen, and, Y.-C. Ku,. 2008. A semantic-expansion approach to personalized knowledge recommendation. Decision Support Systems, 45 (3): 401-412.
35. Lin, D., 1998. An information-theoretic definition of similarity. In Proceedings of the Fifteenth International Conference on Machine Learning, Madison, WI, pp. 296-304.
36. Lu, J., Q. Shambour, Y. Xu, Q. Lin, and, G. Zhang,. 2010. Bizseeker: A hybrid semantic recommendation system for personalized government-to-business e-services. Internet Research, 20 (3): 342-365.
37. Lu, J., Q. Shambour, and, G. Zhang,. 2009. Recommendation technique-based government-to-business personalized e-services. In Proceedings of the 28th North American Fuzzy Information Processing Society Annual Conference (NAFIPS2009), Cincinnati, OH, pp. 1-6.
38. Ma, Z., G. Pant, and, O. R. L. Sheng,. 2007. Interest-based personalized search. ACM Transactions on Information Systems (TOIS), 25 (1): 1-38.
39. Manouselis, N., and, C. Costopoulou,. 2007. Analysis and classification of multi-criteria recommender systems. World Wide Web, 10 (4): 415-441.
40. Markus, M. L., 2000. Paradigm shifts-e-business and business/systems integration. Communications of the Association for Information Systems, 4 (1): 1-45.
41. Martinez, L., L. G. Perez, and, M. J. Barranco,. 2007. A multigranular linguistic content-based recommendation model. International Journal of Intelligent Systems, 22 (5): 419-434.
42. Middleton, S., N. Shadbolt, and, D. De Roure,. 2004. Ontological user profiling in recommender systems. ACM Transactions on Information Systems, 22 (1): 54-88.
43. Mooney, R. J., and, L. Roy,. 2000. Content-based book recommending using learning for text categorization. In Proceedings of the Fifth ACM Conference on Digital Libraries, San Antonio, TX, ACM, pp. 195-204.
44. Pazzani, M. J., and, D. Billsus,. 2007. Content-based recommendation systems. In The Adaptive Web, Edited by, B. Peter, K. Alfred, and, N. Wolfgang,. Springer-Verlag: Heidelberg, pp. 325-341.
45. Porcel, C., and, E. Herrera-Viedma,. 2010. Dealing with incomplete information in a fuzzy linguistic recommender system to disseminate information in university digital libraries. Knowledge-Based Systems, 23 (1): 32-39.
46. Porcel, C., A. G. Lõpez-Herrera, and, E. Herrera-Viedma,. 2009. A recommender system for research resources based on fuzzy linguistic modeling. Expert Systems with Applications, 36 (3, Part 1): 5173-5183.
47. Resnick, P., N. Iacovou, M. Suchak, P. Bergstrom, and, J. Riedl,. 1994. Grouplens: An Open Architecture for Collaborative Filtering of Netnews. In Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, Chapel Hill, NC, pp. 175-186.
48. Resnik, P., 1995. Using information content to evaluate semantic similarity in a taxonomy. In Proceedings of the 14th International Joint Conference on Artificial Intelligence, Montreal, Canada, pp. 448-453.
49. Rhee, S. K., J. Lee, and, M.-W. Park,. 2008. Semantic relevance measure between resources based on a graph structure. In Proceedings of the International Multiconference on Computer Science and Information Technology (IMCSIT), Wista, Poland, pp. 229-236.
50. Ruiz-Montiel, M., and, J. Aldana-Montes,. 2009. Semantically enhanced recommender systems. In On the Move to Meaningful Internet Systems: Otm 2009 Workshops, Edited by, R. Meersman, P. Herrero, and, T. Dillon,. Springer-Verlag: Heidelberg, pp. 604-609.
51. Sarwar, B., G. Karypis, J. Konstan, and, J. Reidl,. 2001. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web, Hong Kong, China, pp. 285-295.
52. Sarwar, B., G. Karypis, J. Konstan, and, J. Riedl,. 2000. Analysis of recommendation algorithms for e-commerce. In Proceedings of the 2nd ACM conference on Electronic commerce, Minneapolis, MN, pp. 158-167.
53. Schubert, P., L. Uwe, and, D. Risch,. 2009. Personalization beyond recommender systems. In Project E-Society: Building Bricks, Edited by, R. Suomi, R. Cabral, and, F. Hampe,. Springer: Boston, pp. 126-139.
54. Serrano-Guerrero, J., E. Herrera-Viedma, J. A. Olivas, A. Cerezo, and, F. P. Romero,. 2011. A google wave-based fuzzy recommender system to disseminate information in university digital libraries 2.0. Information Sciences, 181 (9): 1503-1516.
55. Shambour, Q., and, J. Lu,. 2011a. Government-to-business personalized e-services using semantic-enhanced recommender system. In Egovis 2011, Lncs, Edited by, K. Andersen, E. Francesconi, Å. Grönlund, and, T. van Engers,. Springer: Heidelberg, pp. 197-211.
56. Shambour, Q., and, J. Lu,. 2011b. A hybrid multi-criteria semantic-enhanced collaborative filtering approach for personalized recommendations. In Proceedings of the 2011 IEEE/WIC/ACM International Conference on Web Intelligence (WI'11), Lyon, France, pp. 71-78.
57. Wang, F.-H., and, H.-M. Shao,. 2004. Effective personalized recommendation based on time-framed navigation clustering and association mining. Expert Systems with Applications, 27 (3): 365-377.
58. Wang, Y., W. Dai, and, Y. Yuan,. 2008. Website browsing aid: A navigation graph-based recommendation system. Decision Support Systems, 45 (3): 387-400.
59. Wei, K., J. Huang, and, S. Fu,. 2007. A survey of e-commerce recommender systems. In Proceedings of the International Conference on Service Systems and Service Management, Chengdu, China, pp. 1-5.
60. WONG, R.-Q., and, F.-S. KONG,. 2007. Semantic-enhanced personalized recommender system. In Proceedings of the International Conference on Machine Learning and Cybernetics, Hong Kong, pp. 4069-4074.
61. Yolanda, B.-F., J. P.-A. Jose, G.-S. Alberto, R.-C. Manuel, L.-N. Martin, G.-D. Jorge, F.-V. Ana, and, P. D.-R. Rebeca,. 2008. Exploiting synergies between semantic reasoning and personalization strategies in intelligent recommender systems: A case study. Systems and Software, 81 (12): 2371-2385.
62. Zhang, G., and, J. Lu,. 2003. An integrated group decision-making method dealing with fuzzy preferences for alternatives and individual judgments for selection criteria. Group Decision and Negotiation, 12 (6): 501-515.
63. Zhang, L., M. Zhu, and, W. Huang,. 2009. A framework for an ontology-based e-commerce product information retrieval system. Journal of Computers, 4 (6): 436-443.
64. Zhen, L., G. Q. Huang, and, Z. Jiang,. 2009. Recommender system based on workflow. Decision Support Systems, 48 (1): 237-245.
65. Zotos, N., S. Stamou, P. Tzekou, and, L. Kozanidis,. 2009. Adaptive web site customization. In Metadata and Semantics, Edited by, M.-A. Sicilia, and, M. D. Lytras,. Springer: Heidelberg, pp. 353-363.