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System for Building and Analyzing Preference Models Based on Social Networking Data and SAT Solvers

  • Radosław KlimekEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10842)

Abstract

Discovering and modeling preferences has an important meaning in the modern IT systems, also in the intelligent and multi-agent systems which are context sensitive and should be proactive. The preference modelling enables understanding the needs of objects working within intelligent spaces, in an intelligent city. There was presented a proposal for a system, which, based on logical reasoning and using advanced SAT solvers, is able to analyze data from social networks for preference determination in relation to its own presented offers from different domains. The basic algorithms of the system were presented as well as the validation of practical application.

Keywords

Preference model SAT solvers Social networking data Facebook Twitter 

Notes

Acknowledgments

I would like to thank my students Vadym Perepeliak and Karol Pietruszka (AGH UST, Kraków, Poland) for their valuable cooperation when preparing this work.

References

  1. 1.
    Abdar, M., Yen, N.Y.: Design of a universal user model for dynamic crowd preference sensing and decision-making behavior analysis. IEEE Access 5, 24842–24852 (2017)CrossRefGoogle Scholar
  2. 2.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003). http://dl.acm.org/citation.cfm?id=944919.944937zbMATHGoogle Scholar
  3. 3.
    Harris, Z.: Distributional structure. Word 10(23), 146–162 (1954)CrossRefGoogle Scholar
  4. 4.
    Klimek, R.: Deduction-based formal verification of requirements models with automatic generation of logical specifications. In: Maciaszek, L.A., Filipe, J. (eds.) ENASE 2012. CCIS, vol. 410, pp. 157–171. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-45422-6_11CrossRefGoogle Scholar
  5. 5.
    Klimek, R.: Behaviour recognition and analysis in smart environments for context-aware applications. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC 2015), 9–12 October 2015, City University of Hong Kong, Hong Kong, pp. 1949–1955. IEEE Computer Society (2015)Google Scholar
  6. 6.
    Klimek, R., Kotulski, L.: Proposal of a multiagent-based smart environment for the IoT. In: Augusto, J.C., Zhang, T. (eds.) Workshop Proceedings of the 10th International Conference on Intelligent Environments, 30th June–1st July 2014, Shanghai, China. Ambient Intelligence and Smart Environments, vol. 18, pp. 37–44. IOS Press (2014)Google Scholar
  7. 7.
    Klimek, R., Kotulski, L.: Towards a better understanding and behavior recognition of inhabitants in smart cities. A public transport case. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2015. LNCS (LNAI), vol. 9120, pp. 237–246. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-19369-4_22CrossRefGoogle Scholar
  8. 8.
    Klimek, R., Rogus, G.: Proposal of a context-aware smart home ecosystem. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2015. LNCS (LNAI), vol. 9120, pp. 412–423. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-19369-4_37CrossRefGoogle Scholar
  9. 9.
    Klimek, R., Szwed, P.: Verification of ArchiMate process specifications based on deductive temporal reasoning. In: Proceedings of Federated Conference on Computer Science and Information Systems (FedCSIS 2013), 8–11 September 2013, Kraków, Poland, pp. 1131–1138. IEEE Xplore Digital Library (2013)Google Scholar
  10. 10.
    Kluza, K., Jobczyk, K., Wisniewski, P., Ligeza, A.: Overview of time issues with temporal logics for business process models. In: Ganzha, M., Maciaszek, L.A., Paprzycki, M. (eds.) Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, FedCSIS 2016, 11–14 September 2016, Gdańsk, Poland, pp. 1115–1123 (2016).  https://doi.org/10.15439/2016F328
  11. 11.
    Le Berre, D., Parrain, A.: Sat4j - the Boolean satisfaction and optimization library in Java (2017). http://www.sat4j.org/. Accessed 8 Jun 2017
  12. 12.
    Liu, Y., Xie, Q., Xiong, F.: Recommendations based on collaborative filtering by tag weights. In: 2017 13th International Conference on Semantics, Knowledge and Grids (SKG), pp. 62–68, August 2017Google Scholar
  13. 13.
    Magdum, S.S., Megha, J.V.: Mining online reviews and tweets for predicting sales performance and success of movies. In: 2017 International Conference on Intelligent Computing and Control Systems (ICICCS), pp. 334–339, June 2017Google Scholar
  14. 14.
    Wisniewski, P., Kluza, K., Ligeza, A.: Decision support system for robust urban transport management. In: FedCSIS, pp. 1069–1074 (2017)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.AGH University of Science and TechnologyKrakówPoland

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