Computer Science ›› 2018, Vol. 45 ›› Issue (1): 179-182.doi: 10.11896/j.issn.1002-137X.2018.01.031

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Multi-view Ensemble Framework for Constructing User Profile

FEI Peng, LIN Hong-fei, YANG Liang, XU Bo and Gulziya ANIWAR   

  • Online:2018-01-15 Published:2018-11-13

Abstract: The State Grid users who are sensitive to electric charge often have a strong reaction on electric quantity,electric price,electric charge,payment,arrearage and other electrical service caused by electricity consumption.How to rapidly locate the electric-charge-users plays an important role in reducing customer complaints rate,enhancing customer satisfaction,and establishing a good service image of power supply enterprise.Based on the data of grid users,this paper presented a multi-view ensemble framework for constructing user profile,which can quickly and accurately identify the electric-charge-users.First of all,this paper analyzed the grid users and used two channels to model the users with different characteristics respectivelty.Secondly,this paper presented a variety of feature extraction methods for constructing user multi-source feature systems.Finally,in order to make full use of multi-source features,this paper proposed a multi-view ensemble model based on double Xgboost.This framework was used to obtain the F1 score of 0.90379(The first place) in the “User Profile” contest of 2016 CCF Big Data and Computational Intelligence Contest,validating the effectiveness of the method.

Key words: User profile,Multi-view learning,Model ensemble

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