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Recommendation of Ideas and Antagonists for Crowdsourcing Platform Witology

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

Abstract

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.

Keywords

Crowdsourcing Recommender systems Biclustering Formal concept analysis Witology Social innovation platforms 

Notes

Acknowledgment

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).

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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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