JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2014, Vol. 49 ›› Issue (11): 31-36.doi: 10.6040/j.issn.1671-9352.3.2014.305

Previous Articles     Next Articles

Personalized ranking of Micro-blogging forwarders

KUANG Chong, LIU Zhi-yuan, SUN Mao-song   

  1. State Key Laboratory of Intelligent Technology and Systems; Tsinghua National Laboratory for Information Science and Technology; Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
  • Received:2014-08-28 Revised:2014-10-24 Online:2014-11-20 Published:2014-11-25

Abstract: The repost action is the main way for information spreading in Micro-blogging platform. Nowadays, many works have been done focusing on the repost behaviors' analysis and prediction. However, the problem about how to find the users who are the most likely to repost a given Micro-blog remains unsolved. In this paper, a general predictor, which combines Bayesian Personalized Ranking optimization criterion with Factorization Machines was presented to predict the reposter of a microblog. Furthermore, factors which affect a user to be a reposter were analyzed in details. With these facts, prediction of the reposters over large-scale real datasets was conducted. The experiment proves that this method can improve the effect of the prediction obviously. Meanwhile, method based on pair-wise and feature-related can solve the prediction problem more efficiently.

Key words: Micro-blog, personalized ranking, repost

CLC Number: 

  • TP391
[1] SUH B, HONG L, PIROLLI P, et al. Want to be retweeted? large scale analytics on factors impacting retweet in Twitter network[C]// Proceedings of IEEE 2nd International Conference on Social Computing (Socialcom). Washington: IEEE Computer Society, 2010: 177-184.
[2] HONG Liangjie, DAN O, DAVISON B D. Predicting popular messages in Twitter[C]// Proceedings of the 20th International Conference Companion on World Wide Web. New York: ACM, 2011: 57-58.
[3] FENG Wei, WANG Jianyong.Retweet or not? personalized tweet re-ranking[C]// Proceedings of the 6th ACM International Conference on Web Search and Data Mining. New York: ACM, 2013: 577-586.
[4] HONG Liangjie, DOUMITH A S, DAVISON B D. Co-factorization machines: modeling user interests and predicting individual decisions in Twitter[C]// Proceedings of the 6th ACM International Conference on Web Search And Data Mining. New York: ACM, 2013:557-566.
[5] RENDLE S, FREUDENTHALER C, GANTNER Z, et al. BPR: Bayesian personalized ranking from implicit feedback[C]// Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence. [S.l.]: AUAI, 2009: 452-461.
[6] LUO Zhunchen, OSBORNE M, TANG Jintao, et al. Who will retweet me?: finding retweeters in Twitter[C]// Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2013: 869-872.
[7] RENDLE S. Factorization machines[C]// Proceedings of the 10th IEEE International Conference on Data Mining (ICDM2010). Los Alamitos: IEEE Computer Society, 2010:995-1000.
[8] RENDLE S. Factorization machines with libFM[J]. ACM Transactions on Intelligent Systems and Technology (TIST), 2012, 3(3):57.1-57.22.
[9] BLEI D M, NG A Y, JORDAN M I, et al. Latent dirichlet allocation[J]. The Journal of Machine Learning Research, 2003, 3:993-1022.
[10] WANG Yi, BAI Hongjie, STANTON M, et al. PLDA: parallel latent dirichlet allocation for large-scale applications[C]// Proceedings of Algorithmic Applications in Management(AAIM). Berlin, Heidelberg: Springer, 2009: 301-314.
[11] FAN R E, CHANG K W, HSIEH C J, et al. LIBLINEAR: a library for large linear classification[J]. The Journal of Machine Learning Research, 2008(9):1871-1874.
[12] JOACHIMS T. Training linear SVMs in linear time[C]// Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining(KDD'06). New York: ACM, 2006: 217-226.
[1] ZHANG Zhong-jun, ZHANG Wen-juan, YU Lai-hang, LI Run-chuan. A community division method based on network distance and content similarity in micro-blog social network [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2017, 52(7): 97-103.
[2] HU Mo-zhi, YAO Tian-fang. Recognition of Chinese Micro-blog sentiment polarity and extraction of opinion target [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2016, 51(7): 81-89.
[3] SUN He, LI Shu-qin, L(¨overU)Xue-qiang, LIU Ke-hui. Recognition of geographical entity in city complaints of Micro-blog [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2016, 51(3): 77-85.
[4] HE Yan-xiang, LIU Jian-bo, SUN Song-tao, WEN Wei-dong. Product reviews sentiment classification in Micro-blog based on cascaded conditional random field [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2015, 50(11): 67-73.
[5] WANG Li-ren, YU Zheng-tao, WANG Yan-bing, GAO Sheng-xiang, LI Xian-hui. Micro-blogging topic mining based on supervised LDA user interest model [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2015, 50(09): 36-41.
[6] ZAN Hong-ying, WU Yong-gang, JIA Yu-xiang, NIU Gui-ling. Chinese Micro-blog named entity linking based on multisource knowledge [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2015, 50(07): 9-16.
[7] TANG Bo, CHEN Guang, WANG Xing-ya, WANG Fei, CHEN Xiao-hui. Analysis on new word detection and sentiment orientation in Micro-blog [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2015, 50(01): 20-25.
[8] LIU Pei-yu, ZHANG Yan-hui, ZHU Zhen-fang, XUN Jing. Micro-blog orientation analysis based on emotion symbol [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2014, 49(11): 8-13.
[9] YANG Jia-neng, YANG Ai-min, ZHOU Yong-mei. Sentiment classification method of Chinese Micro-blog based on semantic analysis [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2014, 49(11): 14-21.
[10] SUN Song-tao, HE Yan-xiang, CAI Rui, LI Fei, HE Fei-yan. Comparative study of methods for Micro-blog sentiment evaluation tasks [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2014, 49(11): 43-50.
[11] TIAN Hai-long, ZHU Yan-hui, LIANG Tao, MA Jin, LIU Jing. Research on identificating Chinese micro-blog opinion sentence based on three-way decisions [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2014, 49(08): 58-65.
[12] ZHENG Jian-xing, ZHANG Bo-feng*, YUE Xiao-dong, CHENG Ze-yu. Research on themes recommendation in microblogging
scenario based on neighbor-user profile
[J]. J4, 2013, 48(11): 59-65.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!