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Collective Intelligence for Electric Power Knowledge System

Zixuan Guo, Xuemiao Xu, Guorong Xiao


This paper proposes a collective intelligence model to construct the electric power knowledge database, and the core of the model is the recommend function. Collective intelligence technology is used to do knowledge recommendation in the system. In this paper, Slope one algorithm is initially introduced into the electric power knowledge recommendation and the feedback of the recommendation result will be use for self learning rating. We propose a personalized recommendation method joins weighted slope one algorithm and clustering algorithm, which will improve the calculation accuracy and reduce the computational complexity. We demonstrate the usefulness of the method on a electric power system dataset, and the method has been proved to be useful for obtaining a good recommendation for electric power knowledge learning.


Collective intelligence; Knowledge management; Electric power system.

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