基于E-CARGO的在线社区多对多好友推荐机制研究

doi:10.12052/gdutxb.170040

广东工业大学学报 ›› 2017, Vol. 34 ›› Issue (03): 36-42.doi: 10.12052/gdutxb.170040

• 大数据基础理论与应用专题 • 上一篇下一篇

基于E-CARGO的在线社区多对多好友推荐机制研究

张巍¹, 张思勤¹, 宋静静², 滕少华¹, 刘艳¹

1. 广东工业大学计算机学院, 广东广州 510006;
2. 广东省审计厅计算机审计中心, 广东广州 510630

收稿日期:2017-02-28 出版日期:2017-05-09 发布日期:2017-05-09
作者简介:张巍(1964-),女,副教授,硕士,主要研究方向为数据挖掘.E-mail:weizhang@gdut.edu.cn
基金资助:
国家自然科学基金资助项目（61402118，61673123）；广东省科技计划项目（2013B090200017，2013B010401029，2015B090901016，2016B010108007）；广州市科技计划项目（201508010067，2016201604030034，201604020145）

The Many to Many Friend Recommendation of Online Community Based E-CARGO

Zhang Wei¹, Zhang Si-qin¹, Song Jing-jing², Teng Shao-hua¹, Liu Yan¹

1. School of Computers, Guangdong University of Technology, Guangzhou 510006, China;
2. Computer Audit Center, Guangdong Audit Office, Guangzhou 510360, China

Received:2017-02-28 Online:2017-05-09 Published:2017-05-09

摘要/Abstract

摘要：

好友推荐机制是繁荣在线社区的有效手段，然而单纯为增加用户数及绑定用户关系的过于频繁的推荐方式会引起用户厌烦.为提升用户体验，本文以大型教学与科研协作平台学者网为研究背景，引入基于角色的协同模型E-CARGO对推荐机制进行建模，将好友推荐转化为多对多指派问题，使用带回溯的Kuhn-Munkres算法（KM_B）对好友推荐数与接纳数受限情况下最优推荐指派进行了研究与解决.仿真实验表明，该推荐机制友好、高效、精准，能完善在线社区推荐机制，对在线社会健康发展形成助力.

Abstract:

Friend recommendation is an effective method for establishing an online community. However, over frequent recommendations may be the opposite and become nuisances to users. To improve users' experience, a new method of friend recommendation is proposed via many-to-many assignment. This method limits the number of recommended and accepted friends. It takes as the application background the website http://www.scholat.com/, which is a large higher education and research collaboration platform. Recommendation is modeled via Role-Based Collaboration and its E-CARGO model. After that, the Kuhn-Munkres with Backtracking (KM_B) algorithm is used to solve the optimal assignment of the proposed method. Simulation experiments show that the proposed recommendation method is friendly, efficient and accurate. It can improve the online community recommendation mechanisms, which can support the development of a virtual society.

Key words: online community, friend recommendation, E-CARGO, many to many assignment, KM_B (Kuhn-Munkres) algorithm

中图分类号:

TP311

张巍, 张思勤, 宋静静, 滕少华, 刘艳. 基于E-CARGO的在线社区多对多好友推荐机制研究[J]. 广东工业大学学报, 2017, 34(03): 36-42.

Zhang Wei, Zhang Si-qin, Song Jing-jing, Teng Shao-hua, Liu Yan. The Many to Many Friend Recommendation of Online Community Based E-CARGO[J]. Journal of Guangdong University of Technology, 2017, 34(03): 36-42.

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基于E-CARGO的在线社区多对多好友推荐机制研究

The Many to Many Friend Recommendation of Online Community Based E-CARGO

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