期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

基于网络图模型推荐的随机游走算法改进研究

Improvement Research on the Random Walk Algorithm Based on Network Graph Model
下载PDF
导出
摘要 在网络图模型构建推荐系统中采用随机游走算法有效且易于实现,但存在时间复杂度高的问题.本文通过转移概率矩阵方法,将需要多次迭代的随机游走算法转换成一次矩阵求逆的过程,并使用LU分解方法解决实际应用中大规模数据矩阵求逆对系统资源高消耗的问题,通过对比实验证明了本文方法的有效性. Random walk algorithm can be effective to adopt and easy to practice when constructing a recommendation system in network graph model,but it brings forth a rather high degree of time complexity.Through the approach of transition probability matrix,the present paper simplifies the random walk algorithm with multiple iterations into a process of one-step matrix inversion.Also in the practical usage,LU decomposition is valid to tackle the problem of high resource consumption caused by matrix inversion of Large Scale Data.Lastly,a comparative experiment proves the effectiveness of our method.
作者 陆钊
机构地区 玉林师范学院计算机科学与工程学院
出处 《玉林师范学院学报》 2016年第2期114-118,123,共6页 Journal of Yulin Normal University
基金 广西高校科学技术研究项目 项目编号:KY2015LX300 KY2015YB241 2013LX112
关键词 推荐算法 网络图模型 随机游走算法 LU分解法 recommendation algorithm network graph model random walk algorithm LU decomposition
分类号 TP39 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献11

  • 1孟祥武,刘树栋,张玉洁,胡勋.社会化推荐系统研究[J].软件学报,2015,26(6):1356-1372. 被引量：140
  • 2Guy I,Carmel D.Social recommender systems.[C].In:International Conference Companion on World Wide Web.2014.195-196.
  • 3Li X,Chen H.Recommendation as link prediction in bipartite graphs:A graph kernel-based machine learning approach[J].Decision Support Systems.2013,54(2):880-890.
  • 4Liu J,Zhang F,Song X,et al.What's in a name?:an unsupervised approach to link users across communities[C].In:Sixth Acm International Conference on Web Search & Data Mining.2013.495-504.
  • 5Zafarani R,Abbasi M A,Liu H.Social media mining:an introduction[M].Cambridge University Press,2014.
  • 6Jamali M,Ester M.TrustWalker:a random walk model for combining trust-based and item-based recommendation[J].KDD.2009:397-406.
  • 7Lichtenwalter R.N,Lussier J T,Chawla N V.New perspectives and methods in link prediction.[J].Kdd.2010:243-252.
  • 8Li J,Ma S,Hong S.Recommendation on social network based on graph model[C].In:Control Conference (CCC),201231st Chinese.2012.7548-7551.
  • 9Harryhuang.用PersonalRank实现基于图的推荐算法[EB/OL].http://blog.csdn.net/harryhuangl990/article/ details/10048383,2003-08-13.
  • 10Wikipedia.LU decomposition[EB/OL].https:// en.wikipedia.org/wiki/LU_decomposition,2015-11-24.

二级参考文献93

  • 1Wang Z, Sun LF, Zhu WW, Yang SQ, Li HZ, Wu DP. Joint social and content recommendation for user-generated videos in online social network. IEEE Trans. on Multimedia, 2013,15(3):698-710. [doi: 10.1109/TMM.212.2237022].
  • 2Quijano-Sanchez L, Recio-Garcia J, Diaz-Agudo B. Social factors in group recommender systems. ACM Trans. on Intelligent Systems and Technology, 2013,4(1):Article No.8. [doi: 10.1145/2414425/2414433].
  • 3Social Network Analysis. A brief introduction. 2007. http://orgnet.Com/sna.html.
  • 4Jamali M, Ester M. A transitivity aware matrix factorization model for recommendation in social networks. In: Proc. of the IJCAI. AAAI Press, 2011. 2644-2649. [doi: IO.5591/978-1-57735-516-8/IJCAI11-440].
  • 5Jamali M, Ester M. A matrix factorization technique with trust propagation tbr recommendation in social networks. In: t'roe, ot tlae ReeSys 2010. New York: ACM Press, 2010. 135-142. [doi: 10.1145/1864708.1864736].
  • 6Moghaddam S, Jamali M, Ester M. ETF: Extended tensor factorization model for personalizing prediction of review helpfulness. In: Proc. of the ACM WSDM 2012. New York: ACM Press, 2012. 163-172. Idol: 10.1145/2124295.2124316].
  • 7Hoens TR, Blanton M. A private and reliable recommendation system for social networks. In: Proc. of the IEEE SocialCom. Washington: IEEE Computer Society, 2011. 816-825. Idol: 10.1109/SocialCom.2010.124].
  • 8Berjani B, Strufe T. A recommendation system for spots in location-based online social networks. In: Proc. of the SNS 2011. New York: ACM Press, 201 I. 1-4. [doi: 10.I 145/1989656.1989660].
  • 9Kim HK, Ryu YU. Cho Y. Customer-Driven content recommendation over a network of customers. IEEE Trans. on Systems, Man and Cybernetics, Part A, 2012,42(1):48-56.
  • 10Purushotham S, Liu Y, Kuo CC. Collaborative topic regression with matrix factorization for recommendation systems. In: Proc. of the ICML. 2012. 759-766.

共引文献139

玉林师范学院学报
2016年 第2期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部