Abstract
At present, when developing intellectual systems, one of the most important questions is the construction of a formal description of the data domain. This allows to improve the quality of development. The ontological models are currently used for these purposes. The paper describes the development of a model of the recommendation system based on the ontological approach. When developing recommendatory systems, the usual methods of describing objects are applied, which leads to the impossibility of configuring the system being developed. The purpose of this study was to develop an ontological model for configurable recommender systems. An introduction is given to the topic of ontological modeling, sufficient for understanding the main material of the article. The formal ontology model is presented, the main ontology classes, ontology levels, ontology usage objectives are described. The main principle of modeling the domain object-oriented design is described. Next, the application of ontologies in the recommendation system is described. It describes the conceptual model of the system with UML. A model of the ontology of the data domain description for the development of the recommendatory system was developed. The principal difference of this model from existing models is its customization on the data domain. Using the developed model, it is possible to develop configurable advisory systems. A recommendatory system has been developed using the Python programming language to solve the problem of making recommendations using the developed ontological model of the domain model presentation. Studies were conducted on the effectiveness of modeling the subject area with regard to the compilation of requirements for the recommendation system.
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Kartiev, S.B., Kureychick, V.M. (2018). Algorithm for Building Recommendations for Intelligent Systems. In: Abraham, A., Kovalev, S., Tarassov, V., Snasel, V., Vasileva, M., Sukhanov, A. (eds) Proceedings of the Second International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’17). IITI 2017. Advances in Intelligent Systems and Computing, vol 680. Springer, Cham. https://doi.org/10.1007/978-3-319-68324-9_9
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