On ordered weighted averaging aggregation operators in multicriteria decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
Group decision making and consensus under fuzzy preferences and fuzzy majority
Fuzzy Sets and Systems - Special issue dedicated to Professor Claude Ponsard
Information filtering based on user behavior analysis and best match text retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Analytic properties of maximum entropy OWA operators
Information Sciences—Informatics and Computer Science: An International Journal
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
On the issue of obtaining OWA operator weights
Fuzzy Sets and Systems
Proceedings of the 6th international conference on Intelligent user interfaces
A Taxonomy of Recommender Agents on theInternet
Artificial Intelligence Review
VISCORS: A Visual-Content Recommender for the Mobile Web
IEEE Intelligent Systems
Personalized Course Navigation Based on Grey Relational Analysis
Applied Intelligence
International Journal of Intelligent Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Multiattribute decision aid with extended ISMAUT
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A hybrid approach for personalized recommendation of news on the Web
Expert Systems with Applications: An International Journal
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The goal of raising customer loyalty in electronic commerce requires an emphasis on one-to-one marketing and personalized services. To this end, it is essential to understand individual customer preferences for products. In this paper, we present a method for identifying customer preferences and recommending the most appropriate product. The identification and recommendation of such products are all based on the use of customer's real-time web usage behavior, including activities such as viewing, basket placement, and purchasing of products. Therefore, in this approach, we do not force a customer to explicitly express his or her preference information for particular products but rather capture his or her preferences from data that result from such activities. Information on the web usage behavior for the products determines the ordinal relationships among the products, which express that certain product is preferred to other products across the multiple aspects. The ordinal relationships among the products and the multiple aspects of products lead to the consideration of a multiple-criteria decision-making approach. Thus, the problem eventually results in the identification of weights attached to the multiple criteria in the multidimensional preference space constructed by the ordinal relationships among the products. The derived weights are then used for the prioritization of products that are not included in the navigation behavior due to factors such as time pressure, cognitive burden, and the like.