[1]刘锡文,蒋俊杰.社交网络中基于用户投票的推荐机制[J].东南大学学报(自然科学版),2013,43(2):301-306.[doi:10.3969/j.issn.1001-0505.2013.02.014]
 Liu Xiwen,Jiang Junjie.Recommendation mechanism based on user voting in the social network[J].Journal of Southeast University (Natural Science Edition),2013,43(2):301-306.[doi:10.3969/j.issn.1001-0505.2013.02.014]
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社交网络中基于用户投票的推荐机制()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
43
期数:
2013年第2期
页码:
301-306
栏目:
计算机科学与工程
出版日期:
2013-03-20

文章信息/Info

Title:
Recommendation mechanism based on user voting in the social network
作者:
刘锡文1蒋俊杰2
1东南大学计算机科学与工程学院, 南京 211189; 2上海贝尔股份有限公司, 上海 201206
Author(s):
Liu Xiwen1 Jiang Junjie2
1School of Computer Science and Engineering, Southeast University, Nanjing 211189, China
2Alcatel-Lucent Co., Ltd., Shanghai 201206, China
关键词:
社交网络 推荐机制 热点信息 个性化好友
Keywords:
social network recommendation mechanism hotspot information personalized friends
分类号:
TP393.0
DOI:
10.3969/j.issn.1001-0505.2013.02.014
摘要:
为了改善目前社交网络中热点信息推荐与个性化好友推荐的不足,提出基于用户投票的推荐机制.首先,根据众多用户对某条信息的投票情况评估信息的热度与价值,将用户对信息的浏览、评论、转发等操作以及时间因素与用户主动性投票相结合,提出基于用户投票的热点信息推荐算法. 然后,根据某个用户对众多信息的投票情况评估用户的兴趣,从用户对网络信息的投票以及浏览情况中提取出用户的兴趣度特征,进而提出基于用户投票的个性化好友推荐算法. 最后,针对2个算法进行仿真实验,评估各因素对推荐算法的影响和推荐的有效性. 实验结果表明,基于用户投票的推荐机制可以有效地进行热点信息与个性化好友的推荐.
Abstract:
In order to improve the performance of the hotspot information recommendation and personalized friends recommendation in online social networks, a recommendation mechanism based on user voting is proposed. First, according to a large number of users voting for a certain message, the heat and value of the message can be evaluated. Then a hotspot information recommendation algorithm is proposed combining users operation on the information, including browsing, forwarding and commenting, and the time factor. Secondly, according to one users voting for lots of information, the users interest feature is extracted. Then a personalized friends recommendation algorithm is proposed. Finally, simulation experiments are performed separately to evaluate the effects of different factors on the validity of the two recommendation algorithms. The results show that the proposed recommendation mechanism based on user voting can work effectively and efficiently.

参考文献/References:

[1] Boyd D M, Ellison N B. Social network sites: definition, history, and scholarship[J]. Journal of Computer-Mediated Communication, 2008, 13(1): 210-230.
[2] Snijders T A B, Bunt G G V, Steglich C E G. Introduction to stochastic actor-based models for network dynamics[J]. Social Networks, 2010, 32(1): 44-60.
[3] Granovetter M S. The strength of weak ties[J]. American Journal of Sociology, 1973, 78(6): 1360-1380.
[4] Resnick P, Varian H R. Recommender systems[J]. Communications of the ACM, 1997, 40(3): 56-58.
[5] Balabanovi M, Shoham Y. Fab: content-based, collaborative recommendation[J]. Communications of the ACM, 1997, 40(3): 66-72.
[6] Schafer J B, Konstan J, Riedl J. Recommender systems in e-commerce[C]//Proceedings of the 1st ACM Conference on Electronic Commerce. New York, 1999: 158-166.
[7] 徐海玲, 吴潇, 李晓东, 等. 互联网推荐系统比较研究[J]. 软件学报, 2009, 20(2): 350-362.
  Xu Hailing, Wu Xiao, Li Xiaodong, et al. Comparison study of Internet recommendation system[J]. Journal of Software, 2009, 20(2): 350-362.(in Chinese)
[8] Wikipedia. Wilson score interval[EB/OL].(2011-11-03)[2012-06-31].http://en.wikipedia.org/wiki/Binomial-proportion-confidence-interval.
[9] Wilson E B. Probable inference, the law of succession, and statistical inference[J]. Journal of the American Statistical Association, 1927, 22(158): 209-212.
[10] GroupLens Research. MovieLens data sets[EB/OL].(2011-08-24)[2012-06-31]. http://www.grouplens.org/node/73#attachments.

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备注/Memo

备注/Memo:
作者简介: 刘锡文(1985—),男,硕士生;蒋俊杰(联系人),男,博士,高级工程师,Junjie.Jiang@alcatel-sbell.com.cn.
基金项目: 教育部科技发展中心网络时代的科技论文快速共享专项研究资助项目(20110092110053).
引文格式: 刘锡文,蒋俊杰.社交网络中基于用户投票的推荐机制[J].东南大学学报:自然科学版,2013,43(2):301-306. [doi:10.3969/j.issn.1001-0505.2013.02.014]
更新日期/Last Update: 2013-03-20