A hybrid framework for similarity-based recommendations
by Nikos Karacapilidis, Lefteris Hatzieleftheriou
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 1, No. 1, 2005

Abstract: By exploiting the concept of fuzzy similarity measures, this paper presents a hybrid recommendation framework that builds on the strengths of knowledge-based and collaborative filtering techniques. Following a multi-criteria approach, the proposed framework is able to provide users with a ranked list of alternatives, while it also permits them to submit their evaluations on the existing items of the database. Much attention is given to the extent to which the user evaluation may affect the values of the stored items. The applicability of our approach is demonstrated through an already implemented web-based tool, namely CityGuide, which provides recommendations about visiting different cities of a country. Issues related to the robustness of our framework and the selection of the appropriate similarity measure are also discussed.

Online publication date: Tue, 05-Jul-2005

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