Advertisement

FCA-Based Recommender Models and Data Analysis for Crowdsourcing Platform Witology

  • Dmitry I. IgnatovEmail author
  • Alexandra Yu. Kaminskaya
  • Natalia Konstantinova
  • Alexander Malyukov
  • Jonas Poelmans
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8577)

Abstract

This paper considers a recommender part of the data analysis system for the collaborative platform Witology. It was developed by the joint research team of the National Research University Higher School of Economics and the Witology company. This recommender system is able to recommend ideas, like-minded users and antagonists at the respective phases of a crowdsourcing project. All the recommender methods were tested in the experiments with real datasets of the Witology company.

Keywords

collaborative and crowdsourcing platforms data mining Formal Concept Analysis biclustering recommender systems 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Roth, C., Cointet, J.P.: Social and semantic coevolution in knowledge networks. Social Networks 32, 16–29 (2010)CrossRefGoogle Scholar
  2. 2.
    Yavorsky, R.: Research Challenges of Dynamic Socio-Semantic Networks. In: Ignatov, D., Poelmans, J., Kuznetsov, S. (eds.) CDUD 2011 - Concept Discovery in Unstructured Data. CEUR Workshop Proceedings, vol. 757, pp. 119–122 (2011)Google Scholar
  3. 3.
    Howe, J.: The rise of crowdsourcing. Wired (2006)Google Scholar
  4. 4.
    Ignatov, D.I., Kaminskaya, A.Y., Bezzubtseva, A.A., Konstantinov, A.V., Poelmans, J.: FCA-based models and a prototype data analysis system for crowdsourcing platforms. In: Pfeiffer, H.D., Ignatov, D.I., Poelmans, J., Gadiraju, N. (eds.) ICCS 2013. LNCS (LNAI), vol. 7735, pp. 173–192. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  5. 5.
    Barkow, S., Bleuler, S., Prelic, A., Zimmermann, P., Zitzler, E.: Bicat: a biclustering analysis toolbox. Bioinformatics 22(10), 1282–1283 (2006)CrossRefGoogle Scholar
  6. 6.
    Ignatov, D.I., Kuznetsov, S.O., Poelmans, J., Zhukov, L.E.: Can triconcepts become triclusters? International Journal of General Systems 42(6), 572–593 (2013)CrossRefzbMATHMathSciNetGoogle Scholar
  7. 7.
    Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer-Verlag New York, Inc., Secaucus (1999)CrossRefzbMATHGoogle Scholar
  8. 8.
    Jäschke, R., Hotho, A., Schmitz, C., Ganter, B., Stumme, G.: TRIAS–An Algorithm for Mining Iceberg Tri-Lattices. In: Proceedings of the Sixth International Conference on Data Mining, ICDM 2006, pp. 907–911. IEEE Computer Society, Washington, DC (2006)Google Scholar
  9. 9.
    Ignatov, D.I., Kuznetsov, S.O.: Concept-based Recommendations for Internet Advertisement. In: Belohlavek, R., Kuznetsov, S.O. (eds.) Proc. CLA 2008. CEUR WS, vol. 433, pp. 157–166. Palacky University, Olomouc (2008)Google Scholar
  10. 10.
    Ignatov, D.I., Poelmans, J., Dedene, G., Viaene, S.: A New Cross-Validation Technique to Evaluate Quality of Recommender Systems. In: Kundu, M.K., Mitra, S., Mazumdar, D., Pal, S.K. (eds.) PerMIn 2012. LNCS, vol. 7143, pp. 195–202. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  11. 11.
    Clauset, A., Shalizi, C.R., Newman, M.E.J.: Power-law distributions in empirical data. SIAM Rev. 51(4), 661–703 (2009)CrossRefzbMATHMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Dmitry I. Ignatov
    • 1
    Email author
  • Alexandra Yu. Kaminskaya
    • 1
  • Natalia Konstantinova
    • 4
  • Alexander Malyukov
    • 2
  • Jonas Poelmans
    • 1
    • 3
  1. 1.National Research University Higher School of EconomicsMoscowRussia
  2. 2.WitologyMoscowRussia
  3. 3.KU LeuvenLeuvenBelgium
  4. 4.University of WolverhamptonWolverhamptonUK

Personalised recommendations