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Efficient Product Choice through Ontology-based Recommender Systems

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E-Services

Abstract

In this paper, we examine the issue of customer satisfaction through efficient recommender systems that are based on the semantic web. First, we present an overview of the field of recommender systems, describe the current generation of recommendation methods that are usually classified into two main categories, namely: content-based and collaborative recommendation approaches, and we point out various limitations of current recommendation methods. We then present the basic elements of a prototype recommender system for customer relations management that is based on ontologies and show how the semantic web can provide the underlying engine of recommender systems that are considerably better than previously proposed systems.

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References

  • Adomavicius, Gediminas andAlexander Tuzhilin (2005), “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.

    Article  Google Scholar 

  • Berners-Lee, Tim, James Hendler, and Ora Lassila (2001), “A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities,” Scientific American, 284(5), 34–43.

    Article  Google Scholar 

  • Cayzer, Steve and Uwe Aickelin (2005), “A Recommender System based on Idiotypic Artificial Immune Networks,” Journal of Mathematical Modeling and Algorithms, 4(2), 181–198.

    Article  Google Scholar 

  • Chakrabarty, Anita (2004), “Barking Up the Wrong Tree — Factors Influencing Customer Satisfaction in Retail Banking in the UK,” (accesses November 7, 2006), Available: http://www.managementjournals.com/journals/marketing/article27.htm.

  • Degeratu, Alexandra M., Arvind Rangaswamy, and Jianan Wu (2001), “Consumer Choice Behavior in Online and Traditional Supermarkets: The Effects of Brand Name, Price, and Other Search Attributes,” International Journal of Research in Marketing, 17(1), 55–78.

    Article  Google Scholar 

  • Doukidis, Georgios and Katherine C. Pramataris (2005), “Supply Chains of the Future and Emerging Consumer — Based Electronic Services,” Proceedings Title: Advances in Informatics: 10th Pan-Hellenic Conference on Informatics, Greece, November 2005), 571–581.

    Google Scholar 

  • E-Shops (2006), (accesses November 7, 2006), Available: http://www.hardwaretools. com, http://www.screwfix.com and http://www.toolstation.com.

  • Gangmin, Li, Victoria Uren, Simon B. Shum, Enrico Motta, and John Domingue (2002), “ClaiMaker: Weaving a Semantic Web of Research Papers,” Proceedings Title: International Semantic Web Conference 2002, Sardinia, Italy, 436–441.

    Google Scholar 

  • Graber, Thomas (1993), “A translation approach to portable ontology specifications,” Knowledge Acquisition, 5(2), 199–220.

    Article  Google Scholar 

  • Herlocker, L. Jonathan, Joseph A. Konstan, Loren G. Terveen, and John T. Riedl (2004), “Evaluating Collaborative Filtering Recommender Systems,” A CM Transactions on Information systems, 22(1), 5–53.

    Article  Google Scholar 

  • Horrocks, Ian and Lei Li (2004), “A Software Framework for Matchmaking Based on Semantic Web Technology,” International Journal of Electronic Commerce, 8(4), 39–60.

    Google Scholar 

  • Khalifa, Mohamed and Ning Shen (2005), “Effects of Electronic Customer Relationship Management on Customer Satisfaction: A Temporal Model,” Proceedings Title: 38th Annual Hawaii International Conference on System Sciences, Hawaii, 171a.

    Google Scholar 

  • Liu, Vanessa and Mohamed Khalifa (2002), “Satisfaction with Internet-Based Services,” Proceedings Title: E-comerce Relation Management, 38th Annual Hawaii International Conference on System Sciences, Hawaii, 174.

    Google Scholar 

  • Luke, Sean, Lee Spector and David Rager (1996), “Ontology-Based Knowledge Discovery on the World-Wide Web,” Proceedings Title: Workshop on Internet-based Information Systems, Portland, USA, 96–102.

    Google Scholar 

  • Middleton, Stuart, Harith Alani, Nigel R. Shadbolt, and David C. De Roure (2002), “Exploiting Synergy Between Ontologies and Recommender Systems”, (accesses November 7, 2006), Available: http://arxiv.org/abs/cs.LG/0204012.

  • Noy, Natalya F. and Deborah L. McGuinness (2003), “Ontology Development 101: A Guide to Creating Your First Ontology,” (accesses November 7, 2006), Available: http://protege.stanford.edu/publications/ontology development/ontology 101-noymcguinness.html.

  • Radcliffe, Nicholas J. (1994), “The Algebra of Genetic Algorithms,” Annals of Maths and Artificial Intelligence, 10, 339–384.

    Article  Google Scholar 

  • -(1991), “Equivalence class analysis of genetic algorithms,” Complex Systems, 5(2), 183–205.

    Google Scholar 

  • Reichheld, Frederick F. (1996), The Loyalty Effect: The Hidden Force Behind Growth, Profits, and Lasting Value, Harvard Business School Press.

    Google Scholar 

  • Schäfer, J. Ben, Joseph Konstan, and John Riedl (1999), “Recommender systems in ecommerce,” Proceedings Title: 1st ACM conference on Electronic commerce, 158–166.

    Google Scholar 

  • Shum, Simon B., Enrico Motta, and John Domingue (2000), “ScholOnto: An Ontology-Based Digital Library Server for Research Documents and Discourse,” International Journal on Digital Libraries, 3(3), 237–248.

    Article  Google Scholar 

  • Swearingen, Kirsten and Rashmi Sinha (2001), “Beyond algorithms: An HCI perspective on recommender systems,” (accesses November 7, 2006), Available: http://citeseer.ist.psu.edu/483471.html.

  • Wei, Yan Zheng, Luc Moreau, and Nicholas R. Jennings (2005), “A market-based approach to recommender systems,” ACM Transactions on Information Systems, 23(3), 227–266.

    Article  Google Scholar 

  • W3C (2004), “OWL Web Ontology Language Reference W3C Recommendation,” (accesses November 7, 2006), Available: http://www.w3.org/TR/2004/REC-owlref-20040210.

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© 2007 Deutscher Universitäts-Verlag | GWV Fachverlage GmbH, Wiesbaden

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Tilipakis, N., Douligeris, C. (2007). Efficient Product Choice through Ontology-based Recommender Systems. In: E-Services. DUV. https://doi.org/10.1007/978-3-8350-9614-1_7

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