[1]张琳,张进.基于PPIN的社交网络推荐系统[J].东南大学学报(自然科学版),2017,47(3):478-482.[doi:10.3969/j.issn.1001-0505.2017.03.011]
 Zhang Lin,Zhang Jin.Social network recommendation system based on PPIN[J].Journal of Southeast University (Natural Science Edition),2017,47(3):478-482.[doi:10.3969/j.issn.1001-0505.2017.03.011]
点击复制

基于PPIN的社交网络推荐系统()
分享到:

《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
47
期数:
2017年第3期
页码:
478-482
栏目:
计算机科学与工程
出版日期:
2017-05-20

文章信息/Info

Title:
Social network recommendation system based on PPIN
作者:
张琳张进
南京邮电大学计算机学院, 南京 210003
Author(s):
Zhang Lin Zhang Jin
College of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
关键词:
社交网络 蛋白质相互作用网络 聚类 推荐系统 大数据
Keywords:
social network protein-protein interaction network cluster recommendation system massive data
分类号:
TP393
DOI:
10.3969/j.issn.1001-0505.2017.03.011
摘要:
为了提升海量数据下社交网络推荐系统的性能,将传统聚类方法与蛋白质网络的新特性相结合,提出了一种竞争-抑制节点模型(CINM).该模型将数据的整个处理流程分为节点重构、膜外聚类、膜内聚类及内容推荐4个部分,分别完成数据预处理、数据清洗、精度匹配与数据输出.在数据预处理过程中,通过矩阵运算,将复杂多维数据集构成的用户信息转换成结构化定量数据,并产生数据摘要.数据清理通过判断竞争值来获取用户的特征数据.在精度匹配阶段,基于蛋白质相互作用网络的相似性匹配原理获取相似性最大的一组值,并结合与用户相关联的数据项进行最终内容或关系的推荐.实验结果表明,CINM模型可以通过数据预处理和特征值竞争抑制机制较好地完成数据过滤,从而提高数据处理效率并提升最终推荐结果的精确性.
Abstract:
To improve the performance of the social network recommendation system on massive data, a competition-inhibition node model(CINM)is proposed by combing the traditional clustering methods with the new features of the protein networks. The whole processing flow is divided into four parts including node reconstruction, out-of-band clustering, intra-film clustering and content recommendation, in which data preprocessing, data cleaning, precision matching and data output are performed, respectively. In data preprocessing, the user information with the complex cube is converted into the structured quantitative data by the matrix operation, and the data summary is generated. In data cleaning, the user’s characteristic data are obtained by judging the competition value. During the precision matching phase, a set of values with the greatest similarity are acquired by the similarity matching principle of the protein-protein interaction network. The final content or the relationship can be recommended by the user-association data items. The experimental results show that the CINM model can complete data filtering by data preprocessing and eigenvalue competition prefabrication mechanism to improve the efficiency of data processing and the accuracy of the final recommendation results.

参考文献/References:

[1] Altingovde I S, Subakan Ö N, Ulusoy Ö. Cluster searching strategies for collaborative recommendation systems[J]. Information Processing & Management, 2013, 49(3): 688-697. DOI:10.1016/j.ipm.2012.07.008.
[2] Franceschini A, Szklarczyk D, Frankild S, et al. STRING v9.1: Protein-protein interaction networks, with increased coverage and integration[J]. Nucleic Acids Res, 2013, 41(D1): D808-D815. DOI:10.1093/nar/gks1094.
[3] Pizzuti C, Rombo S E, Marchiori E. Complex detection in protein-protein interaction networks: A compact overview for researchers and practitioners [C]//10th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. Málaga, Spain, 2012: 211-223. DOI:10.1007/978-3-642-29066-4_19.
[4] de Meo P, Nocera A, Terracina G, et al. Recommendation of similar users, resources and social networks in a Social Internetworking Scenario[J]. Information Sciences, 2011, 181(7): 1285-1305. DOI:10.1016/j.ins.2010.12.001.
[5] Sohn J S, Bae U B, Chung I J. Contents recommendation method using social network analysis[J]. Wireless Personal Communications, 2013, 73(4): 1529-1546. DOI:10.1007/s11277-013-1264-z.
[6] Kempe D, Kleinberg J, Tardos É. Influential nodes in a diffusion model for social networks[C]// International Colloquium on Automata, Languages and Programming. Lisbon, Portugal, 2005: 1127-1138. DOI:10.1007/11523468_91.
[7] Leem B, Chun H. An impact of online recommendation network on demand[J]. Expert Systems with Applications, 2014, 41(4): 1723-1729. DOI:10.1016/j.eswa.2013.08.071.
[8] Pandya S, Shah J, Joshi N, et al. A novel hybrid based recommendation system based on clustering and association mining[C]//10th International Conference on Sensing Technology. Nanjing, China, 2016: 1-6. DOI:10.1109/icsenst.2016.7796287.
[9] 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. DOI:10.3724/SP.J.1016.2012.02381.
Jia Dawen, Zeng Cheng, Peng Zhiyong, 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. DOI:10.3724/SP.J.1016.2012.02381. (in Chinese)
[10] 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. DOI:10.3724/SP.J.1016.2013.00349.
Chen Kehan, Han Panpan, Wu Jian. User clustering based social network recommendation[J]. Chinese Journal of Computers, 2013, 36(2): 349-359. DOI:10.3724/SP.J.1016.2013.00349. (in Chinese)
[11] 荣辉桂,火生旭,胡春华,等.基于用户相似度的协同过滤推荐算法[J].通信学报,2014(2):16-24.
  Rong Huigui, Huo Shengxu, Hu Chunhua, et al. User similarity-based collaborative filtering recommendation algorithm[J]. Communication Journal, 2014(2): 16-24.(in Chinese)
[12] 何东晓,周栩,王佐,等.复杂网络社区挖掘——基于聚类融合的遗传算法[J].自动化学报,2010,36(8):1160-1170.
  He Dongxiao, Zhou Xu, Wang Zuo, et al. Community mining in complex networks—Clustering combination based genetic algorithm [J]. Acta Automatica Sinica, 2010, 36(8): 1160-1170.(in Chinese)
[13] 韩毅,方滨兴,贾焰,等.基于密度估计的社会网络特征簇挖掘方法[J].通信学报,2012,33(5):38-48. DOI:10.3969/j.issn.1000-436X.2012.05.005.
Han Yi, Fang Binxing, Jia Yan, et al. Mining characteristic clusters: a density estimation approach[J]. Journal on Communications, 2012, 33(5): 38-48. DOI:10.3969/j.issn.1000-436X.2012.05.005. (in Chinese)
[14] 唐东明,朱清新,杨凡,等.一种有效的蛋白质序列聚类分析方法[J].软件学报,2011,22(8):1827-1837. DOI:10.3724/SP.J.1001.2011.03848.
Tang Dongming, Zhu Qingxin, Yang Fan, et al. Efficient cluster analysis method for protein sequences[J]. Journal of Software, 2011, 22(8): 1827-1837. DOI:10.3724/SP.J.1001.2011.03848. (in Chinese)
[15] 雷秀娟,田建芳.蛋白质相互作用网络的蜂群信息流聚类模型与算法[J].计算机学报,2012,35(1):134-145. DOI:10.3724/SP.J.1016.2012.00134.
Lei Xiujuan, Tian Jianfang. The information flow clustering model and algorithm based on the artificial bee colony mechanism of PPI network[J]. Chinese Journal of Computers, 2012, 35(1): 134-145. DOI:10.3724/SP.J.1016.2012.00134. (in Chinese)
[16] 王智圣,李琪,汪静,等.基于隐式用户反馈数据流的实时个性化推荐[J].计算机学报,2016,39(1):52-64. DOI:10.11897/SP.J.1016.2016.00052.
Wang Zhisheng, Li Qi, Wang Jing, et al. Real-time personalized recommendation based on implicit user feedback data stream[J]. Chinese Journal of Computers, 2016, 39(1): 52-64. DOI:10.11897/SP.J.1016.2016.00052. (in Chinese)

相似文献/References:

[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.[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(3):301.[doi:10.3969/j.issn.1001-0505.2013.02.014]
[2]曹玖新,闵绘宇,徐顺,等.基于启发式和贪心策略的社交网络影响最大化算法[J].东南大学学报(自然科学版),2016,46(5):950.[doi:10.3969/j.issn.1001-0505.2016.05.009]
 Cao Jiuxin,Min Huiyu,Xu Shun,et al.Mixed heuristic and greedy strategies based algorithm for influence maximization in social networks[J].Journal of Southeast University (Natural Science Edition),2016,46(3):950.[doi:10.3969/j.issn.1001-0505.2016.05.009]
[3]曹玖新,崔桂旗,冯雪艳,等.社交网络中基于成本的广告投放策略[J].东南大学学报(自然科学版),2018,48(4):583.[doi:10.3969/j.issn.1001-0505.2018.04.001]
 Cao Jiuxin,Cui Guiqi,Feng Xueyan,et al.Cost-based advertising strategy in social networks[J].Journal of Southeast University (Natural Science Edition),2018,48(3):583.[doi:10.3969/j.issn.1001-0505.2018.04.001]
[4]陈红松,王钢,张鹏.基于Hadoop云平台的新浪微博社交网络关键节点挖掘算法[J].东南大学学报(自然科学版),2018,48(4):590.[doi:10.3969/j.issn.1001-0505.2018.04.002]
 Chen Hongsong,Wang Gang,Zhang Peng.Key nodes mining algorithm in Sina Weibo social network based on Hadoop cloud platform[J].Journal of Southeast University (Natural Science Edition),2018,48(3):590.[doi:10.3969/j.issn.1001-0505.2018.04.002]

备注/Memo

备注/Memo:
收稿日期: 2016-10-12.
作者简介: 张琳(1980—),女,博士,副教授, zhangl@njupt.edu.cn.
基金项目: 国家自然科学基金资助项目(61373017,61402241,61472192,61572260,61572261)、江苏省科技支撑计划资助项目(BE2014718,BE2015702)、江苏省自然科学基金优秀青年基金资助项目(BK20160089)、江苏省普通高校研究生科研创新计划资助项目(CXLX12_0482)、南京邮电大学校级科研基金资助项目(NY217050).
引用本文: 张琳,张进.基于PPIN的社交网络推荐系统[J].东南大学学报(自然科学版),2017,47(3):478-482. DOI:10.3969/j.issn.1001-0505.2017.03.011.
更新日期/Last Update: 2017-05-20