摘要
在社交网络用户推荐研究领域,通过提取用户的行为模式对其进行好友推荐。但是用户的行为是多样性的,在不同的社交平台,用户可能有不同的行为模型。提出跨平台用户推荐模型,同时对用户相关的所有社交网络平台进行建模,最后将用户在所有平台的行为模式进行融合。基于真实的新浪微博数据集和知乎数据集,通过一系列对比实验证明,跨平台用户推荐模型可以更加全面准确地刻画用户行为,更好地进行用户推荐。
In the field of online social networks on user recommendation,researchers extract users’behaviors as much as possible to model the users.However,users may have different likes and dislikes in different social networks.To tackle this problem,a cross-platform user recommendation model was proposed,users would be modeled all-sided.In this study,the Sina micro blog and the Zhihu were investigated in the proposed model,the experimental results show that the proposed model is competitive.Based on the proposed model and the experimental results,it can be known that modeling users in cross-platform online social networks can describe the user more comprehensively and leads to a better recommendation.
作者
彭舰
王屯屯
陈瑜
刘唐
徐文政
PENG Jian;WANG Tuntun;CHEN Yu;LIU Tang;XU Wenzheng(Computer Science School, Sichuan University, Chengdu 610065, China;College of Fundamental Education, Sichuan Normal University, Chengdu 610068, China)
出处
《通信学报》
EI
CSCD
北大核心
2018年第3期147-158,共12页
Journal on Communications
基金
国家自然科学基金资助项目(No.U1333113
No.61602330)
四川省科技支撑计划基金资助项目(No.2014GZ0111)
四川省教育厅科研基金资助项目(No.18ZA0404)~~
关键词
跨平台
用户推荐
在线社交网络
数据挖掘
cross-platform
user recommendation
online social networks
data mining