FUZZY COGNITIVE MAP AS AN INTELLIGENT RECOMMENDER SYSTEM OF WEBSITE RESOURCES

Aleksander Jastriebow

a.jastriebow@tu.kielce.pl
Politechnika Świętokrzyska, Katedra Systemów Informatycznych, Zakład Zastosowań Informatyki (Poland)

Łukasz Kubuś


Politechnika Świętokrzyska, Katedra Systemów Informatycznych, Zakład Zastosowań Informatyki (Poland)

Katarzyna Poczęta


Politechnika Świętokrzyska, Katedra Systemów Informatycznych, Zakład Zastosowań Informatyki (Poland)

Abstract

This paper is devoted to the construction and analysis of the intelligent recommendation system for website resources based on fuzzy cognitive map. The developed system allows to identify resources, which may be interested in a potential user. These resources are determined on the basis of website users activity. Fuzzy cognitive map was develop using the dataset with anonymous collected historical data. The concepts of fuzzy cognitive map are identifiers of resources of website. Weights of the connection between them have been established based on the number of users visiting the resources.


Keywords:

Artificial intelligence, fuzzy cognitive maps, recommender systems

Ahmadi S., Alizadeh S., Forouzideh N., Yeh C., Martin R. L., Papageorgiou E.: ICLA: Imperialist Competitive Learning Algorithm for Fuzzy Cognitive Map. Proceedings of the 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Beijing, China, 2014.
  Google Scholar

Axelrod R.: Structure of Decision: The Cognitive Maps of Political Elites. Princeton University Press, Princeton, New York 1976.
  Google Scholar

Froelich W., Juszczuk P.: Predictive Capabilities of Adaptive and Evolutionary Fuzzy Cognitive Maps – A Comparative Study. Nguyen N.T., Szczerbicki E. (eds.): Intel. Sys. for Know. Management, SCI 252, Springer-Verlag, Heidelberg 2009, 153–174.
  Google Scholar

Froelich W., Papageorgiou E.I.: Extended Evolutionary Learning of Fuzzy Cognitive Maps for the Prediction of Multivariate Time-Series. Papageorgiou E.I.: Fuzzy Cognitive maps for Applied Sciences and Engineering – From fundamentals to extensions and learning algorithms. Springer, Intelligent Systems Reference Library 54, 2014, 121–131.
  Google Scholar

Kannappan A., Papageorgiou E.: A new classification scheme using artificial immune systems learning for fuzzy cognitive mapping. Fuzzy Systems (FUZZ), 2013 IEEE International Conference, 2013, 1–8.
  Google Scholar

Kosko B.: Fuzzy cognitive maps. International Journal of Man-Machine Studies 24(1)/1986, 65–75.
  Google Scholar

Lee K.C., Lee W.J., Kwon O.B., Han J.H., Yu P.I.: Strategic planning simulation based on fuzzy cognitive map knowledge and differential game. Simulation 71(5)/1998, 316–327.
  Google Scholar

Kubuś Ł, Poczęta K.: Learning Fuzzy Cognitive Maps using Evolutionary Algorithms – a comparative study. Transcom Proceedings 2015 section 3, 9–14.
  Google Scholar

Papageorgiou E.I., Parsopoulos K.E., Stylios C.S., Groumpos P.P., Vrahtis M.N.: Fuzzy Cognitive Maps Learning Using Particle Swarm Optimization. Journal of Intelligent Information Systems 25(1)/2005, 95–121.
  Google Scholar

Poczęta K., Yastrebov A.: Analysis of Fuzzy Cognitive Maps with Multi-Step Learning Algorithms in Valuation of Owner-Occupied Homes. IEEE International Conference on Fuzzy Systems (FUZZIEEE), Beijing, China, 2014, 1029–1035.
  Google Scholar

Słoń G.: Application of Models of Relational Fuzzy Cognitive Maps for Prediction of Work of Complex Systems. 13th International Conference ICAISC 2014, Zakopane 2014, 307–318.
  Google Scholar

Stach W., Kurgan L., Pedrycz W.: Data-Driven Nonlinear Hebbian Learning Method for Fuzzy Cognitive Maps. IEEE Int. Conf. on Fuzzy Systems (FUZZ-IEEE), 2008, 1975–1981.
  Google Scholar

Stach W., Kurgan L., Pedrycz W., Reformat M.: Genetic learning of fuzzy cognitive maps. Fuzzy Sets and Systems 153(3)/2005, 371–401.
  Google Scholar

Stach W., Pedrycz W., Kurgan L.A.: Learning of fuzzy cognitive maps using density estimate. IEEE Trans. on Systems, Man, and Cybernetics Part B, 42(3)/2012, 900–912.
  Google Scholar

Breese J.S., Heckerman D., Kadie C.M.: Anonymous Microsoft Web Data Set, http://mlr.cs.umass.edu/ml/datasets/Anonymous+Microsoft+Web+Data, [16.04.2016]
  Google Scholar


Published
2017-12-21

Cited by

Jastriebow, A., Kubuś, Łukasz, & Poczęta, K. . (2017). FUZZY COGNITIVE MAP AS AN INTELLIGENT RECOMMENDER SYSTEM OF WEBSITE RESOURCES. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 7(4), 74–78. https://doi.org/10.5604/01.3001.0010.7365

Authors

Aleksander Jastriebow 
a.jastriebow@tu.kielce.pl
Politechnika Świętokrzyska, Katedra Systemów Informatycznych, Zakład Zastosowań Informatyki Poland

Authors

Łukasz Kubuś 

Politechnika Świętokrzyska, Katedra Systemów Informatycznych, Zakład Zastosowań Informatyki Poland

Authors

Katarzyna Poczęta 

Politechnika Świętokrzyska, Katedra Systemów Informatycznych, Zakład Zastosowań Informatyki Poland

Statistics

Abstract views: 163
PDF downloads: 62