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https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

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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.

Recommendation of Ideas and Antagonists for Crowdsourcing Platform Witology