基于粒计算的多粒度用户画像  

Multi-granularity User Portrait Based on Granular Computing

作　　者：蒋明会 苗夺谦[1,2] 罗晟 赵才荣[1,2] JIANG Minghui;MIAO Duoqian;LUO Sheng;ZHAO Cairong(Department of Computer Science and Technology,Tongji University,Shanghai 201804;Key Laboratory of Embedded System and Service Computing,Ministry of Education,Tongji University,Shanghai 201804)

机构地区：[1]同济大学计算机科学与技术系,上海201804 [2]同济大学嵌入式系统与服务计算教育部重点实验室,上海201804

出　　处：《模式识别与人工智能》2019年第8期691-698,共8页Pattern Recognition and Artificial Intelligence

基　　金：国家重点研发计划项目(No.213);国家自然科学基金项目(No.61673301,61563016);公安部重大专项项目(No.20170004)资助~~

摘　　要：现有的用户画像分析模型使用单一模型单一粒度的学习方式处理异构多源的原始数据,限制分析模型的性能,无法完整展示多层次、多角度的用户画像特征.针对该问题,基于粒计算思想,文中提出多粒度用户画像分析模型.首先,构建数据的多粒度表示结构,粒化原始数据.再根据数据粒度结构,提出基于集成学习的粒度提升算法,用于融合低粒层的数据信息以得到高粒层的数据表示.最后,在多个粒层数据表示上进行用户画像分析,展示一个较全面的用户画像.实验表明,相比单一粒度的用户画像,多粒度的用户画像更全面、立体和丰富.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.

关 键 词：用户画像 多粒度 集成学习 粒度提升 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]