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- W2489592203 abstract "As everyone can enrich or rather impoverish crowd-sourcing contents, it is a crucial need to continuously improve automatic quality contents assessment tools. Structural-based analysis methods developed for such quality prediction purposes generally handle a limited or manually fixed number of families of nodes and relations. This lack of genericity prevents existing algorithms for being adaptable to platforms evolutions. In this work, we propose a generic and adaptable algorithm, called HSQ, generalising various state-of-the-art models and allowing the consideration of graphs defined by an arbitrary number of nodes semantics. Evaluations performed over the two representative crowd-sourcing platforms Wikipedia and Stack Exchange state that the consideration of additional nodes semantics and relations improve the performances of state-of-the-art approaches." @default.
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- W2489592203 date "2016-01-01" @default.
- W2489592203 modified "2023-10-17" @default.
- W2489592203 title "Quality Prediction in Collaborative Platforms: A Generic Approach by Heterogeneous Graphs" @default.
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- W2489592203 doi "https://doi.org/10.1007/978-3-319-44406-2_3" @default.
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