东南大学学报（自然科学版）2017，Vol.47Issue(3)：478-482,5.DOI:10.3969/j.issn.1001-0505.2017.03.011
基于PPIN的社交网络推荐系统
Social network recommendation system based on PPIN
摘要
Abstract
To improve the performance of the social network recommendation system on massive data,a competition-inhibiton 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.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Key words
social network/protein-protein interaction network/cluster/recommendation system/massive data分类
信息技术与安全科学引用本文复制引用
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):478-482,5.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). （61373017,61402241,61472192,61572260,61572261）
