基于粒计算的多粒度用户画像

doi:10.16451/j.cnki.issn1003-6059.201908002

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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.

Key words： User Portrait Multiple Granularities Ensemble Learning Granular Upgrade

Received: 26 June 2019

ZTFLH:

TP 391

Fund:

Supported by National Key Research and Development Programof China(No. 213), National Natural Science Foundation of China(No. 61673301,61563016), Major Project of Ministry of Public Security(No. 20170004)

Corresponding Authors: MIAO Duoqian(Corresponding author), Ph.D., professor. His research interests include artificial intelligence, machine lear-ning, big data analysis and granular computing.

About author:: JIANG Minghui, master student. His research interests include computer vision, deep learning and granular computing.LUO Sheng, Ph.D., lecturer. His research interests include granular computing and machine learning.ZHAO Cairong, Ph.D., associate profe-ssor. His research interests include face re-cognition and computer vision.

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	JIANG Minghui
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Cite this article:

JIANG Minghui,MIAO Duoqian,LUO Sheng等. Multi-granularity User Portrait Based on Granular Computing[J]. , 2019, 32(8): 691-698.

URL:

http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201908002 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2019/V32/I8/691

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