Abstract
With the emergence of new technologies and modern methods of marketing and the increasing intensity of competition among firms and companies for attracting new customers and making them loyal, a novel automatic solution is needed more than ever. The combination of Electronic Customer Relationship Management (E-CRM) and Artificial Intelligence (AI) has appeared as a solution in recent years. Recommending appropriate products to customers according to their needs is one of the methods of CRM. This paper introduces a system named VALA. It is a product recommender system using adjustable customer profiles and a dynamic grouping process which recommends products to each customer dynamically, as his/her preferences change. In other words the User Interface (UI) alters automatically as the customer profile changes. This recommender system combines collaborative filtering and non-collaborative filtering methods in order to come up with useful and unique suggestions for each customer.
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References
Anderson Jr., W.O.: Customer relationship management in an e-business environment. In: Proceedings of 2001 IEEE Change Management and the New Industrial Revolution, IEMC apos (2001)
Appleby, S., Steward, S.: Mobile software agents for control of distributed systems based on principles of social insect behaviour, vol. 2. IEEE, Singapore ICCS apos (1994)
Pani, A.K., Venugopal, P.: Implementing e-CRM using Intelligent Agents on the Internet. In: Service Systems and Service Management. IEEE, Melbourne (2008)
Canuto, A.M., Junior, M.F.G.: Carcara: A Multi- agent System for Web Mining Using Adjustable User Profile and Dynamic Grouping. In: IEEE/WIC/ACM International Conference on Intelligent Agent Technology, 2006. IAT apos, December 18-22, pp. 187–190 (2006)
Chan, P.K.: Constructing Web User Profile: A Noninvasive learnig approach. In: Masand, B., Spiliopoulou, M. (eds.) WebKDD 1999. LNCS (LNAI), vol. 1836, pp. 39–55. Springer, Heidelberg (2000)
Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information tapestry. Communications of the ACM 35(12), 61–70 (1992)
Resnick, P., Varian, H.R.: Recommender systems. Communications of the 6. National Center for Biotechnology Information (1997)
Shardanand, U., Maes, P.: Social information filtering: algorithms for automating word of mouth. In: Proceedings of the Computer Human Interaction- 1995 Conference, Denver, pp. 210–217 (1995)
Yager Ronald, R.: Recommender systems based on representations. In: Machine Perception Artificial Intelligence, Computational Web Intelligence, vol. 58, pp. 3–17 (2004)
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Bahrainian, S.A., Bahrainian, S.M., Salarinasab, M., Dengel, A. (2010). Implementation of an Intelligent Product Recommender System in an e-Store. In: An, A., Lingras, P., Petty, S., Huang, R. (eds) Active Media Technology. AMT 2010. Lecture Notes in Computer Science, vol 6335. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15470-6_19
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DOI: https://doi.org/10.1007/978-3-642-15470-6_19
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