Abstract
This paper proposes an Ontology based CbR system with collective Intelligence for customized iNnformation recommendation (OCRINA), which combines case-based reasoning (CBR) and collective intelligence. Unlike traditional CBR, which consists of 4R (retrieve-reuse-revise-retain), OCRINA is comprised of 5R, which includes the stage of reap additionally, which converts compiled collective intelligence into cases. The system utilizes ontology in order to extract a similar case from the case base, which is a collection of collective intelligence. It also utilizes the technique of Latent Semantic Analysis (LSA) to calculate the extent of the comprehensive similarity between the entirety of all the requests and the whole of all the index values, on the premise that the index values making up the cases will not be independent. Also, in order to demonstrate the superiority of the system, it performs a comparison with the method proposed by Leacock and Chodorow as the best among all the methods calculating the extent of the similarity among concepts.
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Sohn, M., Kang, S.h., Kwon, Y.M. (2011). Customized Travel Information Recommendation Framework Using CBR and Collective Intelligence. In: Jędrzejowicz, P., Nguyen, N.T., Hoang, K. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2011. Lecture Notes in Computer Science(), vol 6923. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23938-0_39
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DOI: https://doi.org/10.1007/978-3-642-23938-0_39
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