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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Gorodetsky, O.N.T.: Ontology and personification of the user profile in the recommended third generation systems. J. Ontol. Des. 3, 7–31 (2014)
Korolev, D.E., Filippov, M.V.: Analysis of collaborative learning algorithms recommendatory systems. Eng. J. Sci. Innovation 6 (2013)
Seguran, T.: Programmable collective mind. M: symbol - Plus (2008)
Regents, A.M.: Meta-level Informational Ensuring the CAD: From Theory to Practice, 176 p. Ulstu, Ulyanovsk (2015)
Ciorascu, C., Ciorascu, I.: Ontological support for information retrieval systems. In: Proceedings of 26th Annual International ACM SIGIR Conference, Workshop on Semantic Web, Toronto, Canada (2003)
Lapshin, V.A.: Ontologies at Computer Systems. Science of World, Moscow (2010)
Butch, G.: Object-Oriented Analysis and Design from Examples of Applications for C++, 2nd edn., 560 p. Nevsky Dialect, St. Petersburg (2001). Transl. from Eng. M. - Publisher Bean
Esposito, D., Saltarello, A.: The Microsoft .NET Architecture of Enterprise Applications, 362 p. (2015). Williams, M.
Muromtsev, D.I.: Ontological Engineering Knowledge at System. SPb SU ITMO, St. Petersburg (2007)
Evans, E.: Domain-Driven Design (DDD). Structuring of Complex Programs, 448 p. (2010). Williams, M.
Fowler, M., Sadaladzh, G.P.: NoSQL Distilled, 192 p. (2012). The ISBN 978-5-8459-1829-1, Williams, M.
Meijer, H.J.M.: The model key to the object of value-mapping the model the data - US patent the App. 12/938.168. The Google Patents (2010)
Fowler, M.: Patterns of Enterprise Application Architecture (Addison-Wesley Signature Series), 544 p. Addison-Wesley, Boston (2003)
Fielding, R., Taylor, R.: Principled design of the modern web architecture. ACM Trans. Inter. Technol. 2, 115–150 (2002)
Shapkin, P.A.: Using ontologies at development of web - applications, custom on substantive area. Inf. Technol. Comput. Syst. 2(C), 44–50 (2009)
DeCandia, G., Hastorun, D., Jampani, M., Kakulapati, G., Lakshman, A., Pilchin, A., Sivasubramanian, S., Vosshall, P., Vogels, W.: Dynamo is: Amazon’s highly available market of value-the key store. In: The ACM Symposium on 21st the Operating Systems’ Principles. Stevenson, The WA (2007)
Kartiev, S.B., Kureichik, V.M., Martynov, A.V.: A parallel algorithm for prediction of short time series. In: Proceedings of the Congress by intelligent systems and Information technology. The IS & of IT 2015. Scientific publication in the 4x volumes, FIZMATLIT the M, pp. 27–47c (2015)
Kartiev, S.B., Kureichik, V.M., Pisarenko, I.: Prediction of time series for analysis of technological processes. In: Actual Problems Hydrolithosphere, pp. 323–331c (2015)
Kureichik, V.M.: Information processing based on ontologies. In: Proceedings of the Congress by Intelligent Systems and Information Technology, the IS & of IT 2015. Scientific publication in the 4x volumes, FIZMATLIT the M, pp. 63–75c (2015)
Kartiev, S.B., Kureichik, V.M.: Classification algorithm, based on the principles of random forests for solving the problem of forecasting. Softw. Prod. Syst. 2016(g2), 11–15c (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-68324-9_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68323-2
Online ISBN: 978-3-319-68324-9
eBook Packages: EngineeringEngineering (R0)