基于E-CARGO的在线社区多对多好友推荐机制研究

张巍; 张思勤; 宋静静; 滕少华; 刘艳

doi:10.12052/gdutxb.170040

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基于E-CARGO的在线社区多对多好友推荐机制研究

大数据基础理论与应用专题 | 更新时间：2026-05-27

- 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
- 广东工业大学学报 2017年34卷第3期页码：36-42
- 作者机构：
- 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
- 基金信息：
  
  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）
- DOI：10.12052/gdutxb.170040
  中图分类号： TP311
- 网络首发：2017-05-09，
  
  纸质出版：2017
- 稿件说明：
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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(3): 36-42.

Zhang Wei, Zhang Si-qin, Song Jing-jing, et al. The Many to Many Friend Recommendation of Online Community Based E-CARGO[J]. 2017, 34(3): 36-42.
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(3): 36-42. DOI： 10.12052/gdutxb.170040.

Zhang Wei, Zhang Si-qin, Song Jing-jing, et al. The Many to Many Friend Recommendation of Online Community Based E-CARGO[J]. 2017, 34(3): 36-42. DOI： 10.12052/gdutxb.170040.

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

）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

) 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.

关键词

Keywords

references

郭磊, 马军, 陈竹敏. 一种信任关系强度敏感的社会化推荐算法[J]. 计算机研究与发展, 2013, 50(9):1805-1813. GUO L, MA J, CHEN Z M. Incorporating item relations for social recommendation[J]. Journal of Computer Research and Development, 2013, 50(9):1805-1813.

杨阿祧, 汤庸, 王江斌, 等. 基于博弈的社会网络个性化好友推荐算法研究[J]. 计算机科学, 2015, 42(9):191-219. YANG A T, TANG Y, WANG J B, et al. Personalized friends recommendation system based on game theory in social network[J]. Computer Science, 2015, 42(9):191-219.

ZHU H, ZHOU M C. Role-based collaboration and its Kernel mechanisms[J]. IEEE Transactions on Systems, Man and Cybernetics, 2006, 36(4):578-589.

ZHU H, ZHOU M C. Efficient role transfer based on Kuhn-Munkres algorithm[J]. IEEE Transactions on Systems, Man and Cybernetics, 2012, 42(2):491-496.

ZHU H, ZHOU M. M-M role-transfer problems and their solutions[J]. IEEE Transactions on Systems, Man and Cybernetics, 2009, 39(2):448-459.

ZHU H, ZHOU M C, ALKINS R. Group role assignment via a Kuhn-Munkres algorithm-based solution[J]. IEEE Transactions on Systems, Man and Cybernetics, 2012, 42(3):739-750.

RESINICK P, VARIAN H R. Recommender systems[J]. Communications of the ACM, 1997, 40(3):56-58.

高灵渲, 张巍, 霍颖翔, 等. 改进的聚类模式过滤推荐算法[J]. 江西师范大学学报(自然科学版), 2012, 36(1):106-110. GAO L X, ZHANG W, HUO Y X, et al. Improved clustering filtering recommendation algorithm[J]. Journal of Jiangxi Normal University(Natural Science), 2012, 36(1):106-110.

黄创光, 印鉴, 汪静, 等. 不确定近邻的协同过滤推荐算法[J]. 计算机学报, 2010, 33(8):1369-1377. HUANG C G, YIN J, WANG J, et al. Uncertain neighbor's collaborative filtering recommendation algorithm[J]. Chinese Journal of Computers. 2010, 33(8):1369-1377.

贾大文, 曾承, 彭智勇, 等. 一种基于用户偏好自动分类的社会媒体共享和推荐方法[J]. 计算机学报, 2012, 35(11):2381-2391. JIA D W, ZENG C, PENG Z Y, et al. A user preference based automatic potential group generation method for social media sharing and recommendation[J]. Chinese Journal of Computers, 2012, 35(11):2381-2391.

刘树栋, 孟祥武. 一种基于移动用户位置的网络服务推荐方法[J]. 软件学报, 2014, 25(11):2556-2574. LIU S D, MENG X W. Approach to network services recommendation based on mobile user's location[J]. Journal of Software, 2014, 25(11):2556-2574.

孙光福, 吴乐, 刘淇, 等. 基于时序行为的协同过滤推荐算法[J]. 软件学报, 2013, 24(11):2721-2733. SUN G F, WU L, LIU Q, et al. Recommendations based on collaborative filtering by exploiting sequential behaviors[J]. Journal of Software, 2013, 24(11):2721-2733.

陈克寒, 韩盼盼, 吴健. 基于用户聚类的异构社交网络推荐算法[J]. 计算机学报, 2013, 36(2):349-359. CHEN K H, HAN P P, WU J. User clustering based social network recommendation[J]. Chinese Journal of Computers, 2013, 36(2):349-359.

李美子, 黄震华, 向阳, 等. 社交网络中基于信任评估的推荐控制模型[J]. 同济大学学报(自然科学版), 2014, 42(7):1117-1122. LI M Z, HUANG Z H, XIANG Y, et al. Trust evaluation-based recommendation control model in social network site[J]. Journal of Tongji University (Natural Science), 2014, 42(7):1117-1122.

MA H, KING I, LYU M. Learning to recommend with social trust ensemble[C]//SIGIR'09 Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval. Boston, MA, USA:ACM, 2009:203-210.

MA H, YANG H, LYU M, et al. Sorec:Social recommendation using probabilistic matrix factorization[C]//CIKM'08 Proceedings of the 17th ACM Conference on Information and Knowledge Management. Napa Valley, California, USA:ACM, 2008:931-940.

王玙, 高琳. 基于社交圈的在线社交网络朋友推荐算法[J]. 计算机学报, 2014, 37(4):801-808. WANG Y, GAO L. Social circle-based algorithm for friend recommendation in online social networks[J]. Chinese Journal of Computers, 2014, 37(4):801-808.

张志军, 刘弘. 上下文感知的移动社交网络推荐算法研究[J]. 模式识别与人工智能, 2015, 28(5):404-410. ZHANG Z J, LIU H. Research on context-awareness mobile SNS recommendation algorithm[J]. Pattern Recognition and Artificial Intelligence, 2015, 28(5):404-410.

SILVA N B, TSANG I R, CAVALCANTI G D C, et al. A graph-based friend recommendation system using genetic algorithm[C]//IEEE Congress on Evolutionary Computation.[S.l.]:IEEE, 2011.

蒋盛益, 杨博泓, 吴美玲. 基于快速社区检测的协同过滤推荐算法[J]. 广西大学学报(自然科学版), 2013, 38(6):1408-1412. JIANG S Y, YANG B H, WU M L. Collaborative filter recommendation algorithm based on fast community detection[J]. Journal of Guangxi University(Natural Science Edition), 2013, 38(6):1408-1412.

王勇, 易庭. 基于距离衰减和评分趋势改进的协同推荐算法[J]. 广东工业大学学报, 2015, 32(2):38-42. WANG Y, YI T. A distance decay and score trends improved collaborative recommendation algorithm[J]. Journal of Guangdong University of Technology, 2015, 32(2):38-42.

汪岭, 傅秀芬, 王晓牡. 基于大数据集的混合动态协同过滤算法研究[J]. 广东工业大学学报, 2014, 31(3):44-48. WANG L, FU X F, WANG X M. Hybrid dynamic collaborative filtering algorithm based on big data sets[J]. Journal of Guangdong University of Technology, 2014, 31(3):44-48.

ZHU H, LIU D N, ZHANG S Q, et al. Solving the many to many assignment problem by improving the Kuhn-Munkres algorithm with backtracking[J]. Theoretical Computer Science, 2016, 618(3):30-41.

刘艳. 基于E-CARGO的社会网络好友推荐机制研究与仿真[D]. 广州:广东工业大学计算机学院, 2016.

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