Abstract
Personalized profiles that describe user behaviour and preferences are encountered in many applications, ranging from recommender systems or user agents (Web browsers) to one-to-one marketing. User profiling is a crucial step in providing accurate product recommendations to the end users. Once groups of people with similar preferences are identified, information, products, or services tailored to group’s needs, can be delivered. The aim of this paper is to clarify the role of utility functions in the formation of user profiles. Thus, the clustering behaviour of customers’ preferences is investigated by means of clustering algorithms and preference modelling. It is shown in this work that the incorporation of a Multi-criteria methodology prior to the application of a clustering algorithm constitutes a fundamental step for the formation of more accurate user profiles in terms of compactness and separation of the groups.
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Acknowledgments
The authors are grateful to professors, M. Doumpos, N. Vlassis and A. Doulamis who contributed their time and knowledge to this work and also to the anonymous referees for their valuable comments. This work is part of the 03ED375 research project, implemented within the framework of the “Reinforcement Programme of Human Research Manpower” (PENED) and co-financed by National and Community Funds (75% from E.U.-European Social Fund and 25% from the Greek Ministry of Development-General Secretariat of Research and Technology).
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Lakiotaki, K., Delias, P., Sakkalis, V. et al. User profiling based on multi-criteria analysis: the role of utility functions. Oper Res Int J 9, 3–16 (2009). https://doi.org/10.1007/s12351-008-0024-4
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DOI: https://doi.org/10.1007/s12351-008-0024-4