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Multi-granularity User Portrait Based on Granular Computing
Author(s): 
Pages: 691-698
Year: Issue:  8
Journal: Pattern Recognition and Artificial Intelligence

Keyword:  User PortraitMultiple GranularitiesEnsemble LearningGranular Upgrade;
Abstract: Single model with single granularity is employed to process multi-sources heterogeneous raw data in the existing user portrait models. The performance of the analytic model is limited and the multi-level and multi-angle user portrait features cannot be fully displayed. Aiming at this problem, based on the idea of granular computing, a multi-granularity user portrait model is proposed. Firstly, a multi-granular representation structure of the data is constructed to granulate the raw data. Then, according to the data granularity structure, a granularity upgrade algorithm based on ensemble learning is proposed. Low-level data information is fused to obtain high-level data representation. Finally, user portrait analysis is carried out at multi-level data representation to show a more comprehensive portrait. Experiments show that the user portrait with multiple granularities is more comprehensive, stereoscopic and richer than the single granularity user portrait.
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