Abstract
Recommender systems address a variety of ubiquitous commerce needs. In ubiquitous commerce, contextual information must be incorporated into the recommendation process. The total amount of information is larger due to the greater number of contexts in multicontext environments. Multicontext therefore requires a more accurate and rapid recommendation method. This paper proposes a multicontext-aware recommendation for ubiquitous commerce using consumer’s preferences and behavior as a weighting factor. The recommendation method is described and an application prototype is presented. Several experiments are performed and the results verify that the proposed method’s recommendation performance is better than other existing method.
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Kwon, J., Kim, S. (2006). Multicontext-Aware Recommendation for Ubiquitous Commerce. In: Madria, S.K., Claypool, K.T., Kannan, R., Uppuluri, P., Gore, M.M. (eds) Distributed Computing and Internet Technology. ICDCIT 2006. Lecture Notes in Computer Science, vol 4317. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11951957_26
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DOI: https://doi.org/10.1007/11951957_26
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