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Combining collaborative filtering with personal agents for better recommendations
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Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Agent-oriented technology in support of e-business
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Recommending or persuading?: the impact of a shopping agent's algorithm on user behavior
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The Knowledge Engineering Review
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On causal inference in fuzzy cognitive maps
IEEE Transactions on Fuzzy Systems
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Information Sciences: an International Journal
Review: Personalizing recommendations for tourists
Telematics and Informatics
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SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Scenario analysis using Bayesian networks: A case study in energy sector
Knowledge-Based Systems
Personalization of web-based systems based on computational intelligence modeling
CEA'10 Proceedings of the 4th WSEAS international conference on Computer engineering and applications
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WSEAS Transactions on Information Science and Applications
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The MARS --- a multi-agent recommendation system for games on mobile phones
KES-AMSTA'12 Proceedings of the 6th KES international conference on Agent and Multi-Agent Systems: technologies and applications
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Bi-linear adaptive estimation of Fuzzy Cognitive Networks
Applied Soft Computing
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Expert Systems with Applications: An International Journal
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There is an increasing need for various e-service, e-commerce and e-business sites to provide personalized recommendations to on-line customers. This paper proposes a new type of personalized recommendation agents called fuzzy cognitive agents. Fuzzy cognitive agents are designed to give personalized suggestions based on the user's current personal preferences, other user's common preferences, and expert's domain knowledge. Fuzzy cognitive agents are able to represent knowledge via extended fuzzy cognitive maps, to learn users' preferences from most recent cases and to help customers make inferences and decisions through numeric computation instead of symbolic and logic deduction. A case study is included to illustrate how personalized recommendations are made by fuzzy cognitive agents in e-commerce sites. The case study demonstrates that the fuzzy cognitive agent is both flexible and effective in supporting e-commerce applications.