Abstract
Case-based reasoning can resolve new problems based on remembering and adapting the solution of similar problems. Before a CBR system can solve new problems it must be provided with an initial case base covering the problem space with a sufficient number of representative seed cases with solutions that are known to be correct. We use a CBR module to recommend leisure plans in Madrid based on user preferences and contextual information. This paper deals with the problem of how to build and evaluate an initial case base of leisure experiences in Madrid for the recommender system.
Supported by the Spanish Committee of Economy and Competitiveness (TIN2014-55006-R, TIN2017-87330-R); the UCM (Group 921330) and the funding provided by Banco Santander, in cooperation with UCM, in the form of a predoctoral scholarship (CT17/17-CT17/18).
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Jorro-Aragoneses, J.L., Jimenez-Díaz, G., Recio-García, J.A., Díaz-Agudo, B. (2018). Case Base Elicitation for a Context-Aware Recommender System. In: Cox, M., Funk, P., Begum, S. (eds) Case-Based Reasoning Research and Development. ICCBR 2018. Lecture Notes in Computer Science(), vol 11156. Springer, Cham. https://doi.org/10.1007/978-3-030-01081-2_12
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