Recommendation of Ideas and Antagonists for Crowdsourcing Platform Witology

  • Dmitry I. IgnatovEmail author
  • Maria Mikhailova
  • Alexandra Yu. Zakirova
  • Alexander Malioukov
Part of the Communications in Computer and Information Science book series (CCIS, volume 505)


This paper introduces several recommender methods for crowdsourcing platforms. These methods are based on modern data analysis approaches for object-attribute data, such as Formal Concept Analysis and biclustering. The use of the proposed techniques is illustrated by the results of recommendation of ideas and antagonists for crowdsourcing platform Witology. In particular we show how the quality of antagonists recommender can be improved by usage of biclusters as focal areas for distance and similarity calculation.


Crowdsourcing Recommender systems Biclustering Formal concept analysis Witology Social innovation platforms 



The study was conducted in the framework of the Basic Research Program at the National Research University Higher School of Economics (HSE) in 2013 and 2014 and in the Laboratory of Intelligent Systems and Structural Analysis at HSE as a part of the project “Mathematical Models, Algorithms, and Software Tools for Intelligent Analysis of Structural and Textual Data”. First author was also supported by Russian Foundation for Basic Research (grant #13-07-00504).


  1. 1.
    Rozwell, C., Harris, K., Mesaglio, M.: Who’s who in innovation management technology. Gartner (2010)Google Scholar
  2. 2.
    Howe, J.: The rise of crowdsourcing. Wired Mag. 14(6), 1–4 (2006)Google Scholar
  3. 3.
    Roth, C.: Generalized preferential attachment: Towards realistic socio-semantic network models. In: ISWC 4th International Semantic Web Conference, Workshop on Semantic Network Analysis, CEUR-WS Series (ISSN 1613–0073), vol. 171, pp. 29–42, Galway, Ireland (2005)Google Scholar
  4. 4.
    Cointet, J.P., Roth, C.: Socio-semantic dynamics in a blog network. In: CSE, vol. 4, pp. 114–121. IEEE Computer Society (2009)Google Scholar
  5. 5.
    Roth, C., Cointet, J.P.: Social and semantic coevolution in knowledge networks. Soc. Netw. 32, 16–29 (2010)CrossRefGoogle Scholar
  6. 6.
    Yavorsky, R.: Research challenges of dynamic socio-semantic networks. In: Ignatov, D., Poelmans, J., Kuznetsov, S., (eds.) CEUR Workshop proceedings, CDUD 2011 - Concept Discovery in Unstructured Data, vol. 757, pp. 119–122 (2011)Google Scholar
  7. 7.
    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, vol. 7735, pp. 173–192. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  8. 8.
    Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer-Verlag New York Inc., Secaucus (1999) CrossRefGoogle Scholar
  9. 9.
    Ambati, V., Vogel, S., Carbonell, J.G.: Towards task recommendation in micro-task markets. In: (2011) Human Computation, Papers from the 2011 AAAI Workshop, vol. WS-11-11. AAAI, San Francisco, August 8, 2011Google Scholar
  10. 10.
    Yuen, M.C., King, I., Leung, K.S.: Taskrec: A task recommendation framework in crowdsourcing systems. Neural Process. Lett. 41(2), 1–16 (2014)Google Scholar
  11. 11.
    Lin, C.H., Kamar, E., Horvitz, E.: Signals in the silence: Models of implicit feedback in a recommendation system for crowdsourcing. In: Brodley, C.E., Stone, P. (eds.) Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, pp. 908–915. AAAI Press, Canada (2014)Google Scholar
  12. 12.
    Geiger, D., Schader, M.: Personalized task recommendation in crowdsourcing information systems - current state of the art. Decis. Support Syst. 65, 3–16 (2014)CrossRefGoogle Scholar
  13. 13.
    Chander, D., Bhattacharya, S., Celis, L.E., Dasgupta, K., Karanam, S., Rajan, V., Gupta, A.: Crowdutility: a recommendation system for crowdsourcing platforms. In: Bigham, J.P., Parkes, D.C., (eds.) Proceedings of the Seconf AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2014, November 2–4, 2014. AAAI, Pittsburgh (2014)Google Scholar
  14. 14.
    Ignatov, D.I., Kaminskaya, A.Y., Konstantinova, N., Malioukov, A., Poelmans, J.: FCA-based recommender models and data analysis for crowdsourcing platform witology. In: Hernandez, N., Jäschke, R., Croitoru, M. (eds.) ICCS 2014. LNCS, vol. 8577, pp. 287–292. Springer, Heidelberg (2014) Google Scholar
  15. 15.
    Desrosiers, C., Karypis, G.: A comprehensive survey of neighborhood-based recommendation methods. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 107–144. Springer, Heidelberg [36] (2011)Google Scholar
  16. 16.
    Koren, Y., Bell, R.M.: Advances in collaborative filtering. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 145–186. Springer, Heidelberg [36] (2011)Google Scholar
  17. 17.
    Ricci, F., Rokach, L., Shapira, B.: Introduction to recommender systems handbook. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 1–35. Springer, Heidelberg [36] (2011)Google Scholar
  18. 18.
    Bezzubtseva, A., Ignatov, D.I.: A new typology of collaboration platform users. In: Tagiew, R., Ignatov, D.I., Neznanov, A.A., Poelmans, J. (eds.) CEUR Workshop proceedings, EEML 2012 - Experimental Economics and Machine Learning, vol. 757, pp. 9–19 (2012)Google Scholar
  19. 19.
    Ignatov, D.I., Poelmans, J., Dedene, G., Viaene, S.: A new cross-validation technique to evaluate quality of recommender systems. In: Mitra, S., Mazumdar, D., Kundu, M.K., Pal, S.K. (eds.) PerMIn 2012. LNCS, vol. 7143, pp. 195–202. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  20. 20.
    Radlinski, F., Hofmann, K.: Practical online retrieval evaluation. In: Serdyukov, P., Braslavski, P., Kuznetsov, S., Kamps, J., Ruger, S., Agichtein, E., Segalovich, I., Yilmaz, E. (eds.) Advances in Information Retrieval. LNCS, vol. 7814, pp. 878–881. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  21. 21.
    Barkow, S., Bleuler, S., Prelic, A., Zimmermann, P., Zitzler, E.: BicAT: a biclustering analysis toolbox. Bioinformatics 22(10), 1282–1283 (2006)CrossRefGoogle Scholar
  22. 22.
    Ignatov, D.I., Kaminskaya, A.Y., Kuznetsov, S., Magizov, R.A.: Method of biclusterzation based on object and attribute closures. In: Proceedings of 8-th international Conference on Intellectualization of Information Processing (IIP 2011), pp. 140–143. MAKS Press, Cyprus, Paphos, 17–24 October 2010 (in Russian)Google Scholar
  23. 23.
    Ignatov, D.I., Kuznetsov, S.O., Magizov, R.A., Zhukov, L.E.: From triconcepts to triclusters. In: Hepting, D.H., Ślȩzak, D., Mirkin, B.G., Kuznetsov, S.O. (eds.) RSFDGrC 2011. LNCS, vol. 6743, pp. 257–264. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  24. 24.
    Ignatov, D.I., Kuznetsov, S.O., Poelmans, J., Zhukov, L.E.: Can triconcepts become triclusters? Int. J. Gen. Syst. 42(6), 572–593 (2013)MathSciNetCrossRefGoogle Scholar
  25. 25.
    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 (2006)Google Scholar
  26. 26.
    du Boucher-Ryan, P., Bridge, D.G.: Collaborative recommending using formal concept analysis. Knowl. Based Syst. 19(5), 309–315 (2006)CrossRefGoogle Scholar
  27. 27.
    Symeonidis, P., Nanopoulos, A., Papadopoulos, A.N., Manolopoulos, Y.: Nearest-biclusters collaborative filtering based on constant and coherent values. Inf. Retr. 11(1), 51–75 (2008)CrossRefGoogle Scholar
  28. 28.
    Ignatov, D.I., Kuznetsov, S.O.: Concept-based recommendations for internet advertisement. In: Belohlavek, R., Kuznetsov, S.O (eds.) Proceedings of CLA 2008, CEUR WS., Palacky University, vol. 433, pp. 157–166, Olomouc (2008)Google Scholar
  29. 29.
    Ignatov, D., Poelmans, J., Zaharchuk, V.: Recommender system based on algorithm of bicluster analysis RecBi. In: Ignatov, D., Poelmans, J., Kuznetsov, S. (eds.) CEUR Workshop proceedings, CDUD 2011 - Concept Discovery in Unstructured Data, vol. 757, pp. 122–126 (2011)Google Scholar
  30. 30.
    Ignatov, D.I., Kuznetsov, S.O., Poelmans, J.: Concept-based biclustering for internet advertisement. In: (2012) Vreeken, J., Ling, C., Zaki, M.J., Siebes, A., Yu, J.X., Goethals, B., Webb, G.I., Wu, X. (eds.) 12th IEEE International Conference on Data Mining Workshops, ICDM Workshops, pp. 123–130. IEEE Computer Society, Brussels, 10 December 2012Google Scholar
  31. 31.
    Nanopoulos, A., Rafailidis, D., Symeonidis, P., Manolopoulos, Y.: MusicBox: personalized music recommendation based on cubic analysis of social tags. IEEE Trans. Audio Speech Lang. Process. 18(2), 407–412 (2010)CrossRefGoogle Scholar
  32. 32.
    Jelassi, M.N., Ben Yahia, S., Mephu Nguifo, E.: A personalized recommender system based on users’ information in folksonomies. In: Proceedings of the 22nd International Conference on World Wide Web Companion. WWW 2013 Companion, Republic and Canton of Geneva, Switzerland, International World Wide Web Conferences Steering Committee, pp. 1215–1224 (2013)Google Scholar
  33. 33.
    Ignatov, D.I., Nenova, E., Konstantinova, N., Konstantinov, A.V.: Boolean matrix factorisation for collaborative filtering: an FCA-based approach. In: Agre, G., Hitzler, P., Krisnadhi, A.A., Kuznetsov, S.O. (eds.) AIMSA 2014. LNCS, vol. 8722, pp. 47–58. Springer, Heidelberg (2014) Google Scholar
  34. 34.
    Alqadah, F., Reddy, C., Hu, J., Alqadah, H.: Biclustering neighborhood-based collaborative filtering method for top-n recommender systems. Knowl. Inf. Syst. 44(2), 1–17 (2014)Google Scholar
  35. 35.
    Kuznetsov, S.O.: On stability of a formal concept. Ann. Math. Artif. Intell. 49(1–4), 101–115 (2007)MathSciNetCrossRefGoogle Scholar
  36. 36.
    Ricci, F., Rokach, L., Shapira, B., Kantor, P.B.: Recommender Systems Handbook. Springer, Hedielberg (2011) CrossRefGoogle Scholar

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Authors and Affiliations

  • Dmitry I. Ignatov
    • 1
    Email author
  • Maria Mikhailova
    • 1
  • Alexandra Yu. Zakirova
    • 1
    • 3
  • Alexander Malioukov
    • 2
  1. 1.National Research University Higher School of EconomicsMoscowRussia
  2. 2.WitologyMoscowRussia
  3. 3.YandexMoscowRussia

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