基于跨平台的在线社交网络用户推荐研究

彭舰; 王屯屯; 陈瑜; 刘唐; 徐文政

doi:10.11959/j.issn.1000-436x.2018044

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基于跨平台的在线社交网络用户推荐研究

论文Ⅱ：学术论文 | 更新时间：2024-06-05

- 基于跨平台的在线社交网络用户推荐研究
- User recommendation based on cross-platform online social networks
- 通信学报 2018年39卷第3期页码：147-158
- 作者机构：
  
  1. 四川大学计算机学院，四川成都 610065
  2. 四川师范大学基础教学学院，四川成都 610068
- 作者简介：
  
  [ "彭舰（1970-），男，四川成都人，博士，四川大学教授，主要研究方向为大数据、传感器计算、移动计算等。" ]
  [ "王屯屯（1992-），男，河南安阳人，四川大学硕士生，主要研究方向为数据挖掘、推荐系统、用户行为建模等。" ]
  [ "陈瑜（1974-），男，四川成都人，博士，四川大学讲师，主要研究方向为进化计算、机器学习等。" ]
  [ "刘唐（1980-），男，四川乐山人，博士，四川师范大学副教授，主要研究方向为无线传感器网络、无线能量传输等。" ]
  [ "徐文政（1985-），男，四川成都人，博士，四川大学副研究员，主要研究方向为社交网络、物联网、移动计算。" ]
- 基金信息：
  
  国家自然科学基金资助项目(U1333113);国家自然科学基金资助项目(61602330);四川省科技支撑计划基金资助项目(2014GZ0111);四川省教育厅科研基金资助项目(18ZA0404)
- DOI：10.11959/j.issn.1000-436x.2018044
  中图分类号： TP311
- 网络出版日期：2018-03，
  
  纸质出版日期：2018-03-25
- 稿件说明：
移动端阅览
彭舰, 王屯屯, 陈瑜, 等. 基于跨平台的在线社交网络用户推荐研究[J]. 通信学报, 2018,39(3):147-158.

Jian PENG, Tuntun WANG, Yu CHEN, et al. User recommendation based on cross-platform online social networks[J]. Journal on communications, 2018, 39(3): 147-158.
彭舰, 王屯屯, 陈瑜, 等. 基于跨平台的在线社交网络用户推荐研究[J]. 通信学报, 2018,39(3):147-158. DOI： 10.11959/j.issn.1000-436x.2018044.

Jian PENG, Tuntun WANG, Yu CHEN, et al. User recommendation based on cross-platform online social networks[J]. Journal on communications, 2018, 39(3): 147-158. DOI： 10.11959/j.issn.1000-436x.2018044.

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。

Abstract

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.

关键词

Keywords

references

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