Arabian Journal for Science and Engineering

, Volume 40, Issue 5, pp 1443–1453 | Cite as

Information Recommendation Between User Groups in Social Networks

Research Article - Computer Engineering and Computer Science
First Online:
Received:
Accepted:

Abstract

Information recommendation between different user groups has recently received a lot of attention in the information service community. However, we find that obtaining the exact optimal recommendation solution is an NP-hard problem. Based on the above finding, in this paper, we present an efficient method achieving approximate optimal recommendation solution (AAORS) to reduce this NP-hard problem to an equivalent extended Steiner tree problem and obtain the approximate optimal recommendation solution appIRS in polynomial time. We theoretically prove that the global trust value of appIRS is at least 63 % of that obtained for the exact optimal solution optIRS. Moreover, in real applications, based on a computed index of reputation gain, we also adjust the recommendation solution produced by the AAORS method in polynomial time and obtain the optimal recommendation solution which satisfies the global reputation constraint. The detailed theoretical analyses and extensive experiments demonstrate that our proposed methods are both efficient and effective.

Keywords

Social network Information recommendation User group Approximation algorithm Steiner tree 
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References

  1. 1.
    Baglioni, M.; Geraci, F.; Pellegrini, M.; Lastres, E.: Fast exact computation of betweenness centrality in social networks. In: Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 450–456 (2012)Google Scholar
  2. 2.
    Hassan, S.; Sanger, J.; Pernul, G.; SoDA: dynamic visual analytics of big social data. In: Proceedings of the International Conference on Big Data and Smart Computing, pp. 183–188 (2014)Google Scholar
  3. 3.
    Lilac A., Dalal A., Wejdan A.: A comparison between ontology-based and translation-based semantic search engines for arabic blogss. Arab. J. Sci. Eng. 38(11), 2985–2992 (2013)CrossRefGoogle Scholar
  4. 4.
    Gong, X.; Chen, X.; Zhang, J.: Social group utility maximization game with applications in mobile social networks. In: Proceedings of the 51st Annual Allerton Conference on the Communication, Control, and Computing, pp. 1496–1500 (2013)Google Scholar
  5. 5.
    Xiang, R.; Neville, J.; Rogati, M.: Modeling relationship strength in online social network. In: Proceedings of the 19th International Conference on World Wide Web, pp. 981–990 (2010)Google Scholar
  6. 6.
    Borgatti S.P., Mehra A., Brass D.J.: Network analysis in the social sciences. Science 323(5916), 892–895 (2009)CrossRefGoogle Scholar
  7. 7.
    Amershi, S.; Fogarty, J.; Weld, D.: Regroup: interactive machine learning for on-demand group creation in social networks. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 21–30 (2012)Google Scholar
  8. 8.
    Gimenes, G.P.; Gualdron, H.; Raddo, T.R.; Rodrigues, J.F.: Supervised-learning link recommendation in the DBLP co-authoring network. In: Proceedings of IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 563–568 (2014)Google Scholar
  9. 9.
    Semenov, A.; Veijalainen, J.; Hajeer, M.; Dasgupta, D.: Political communities in Russian portion of LiveJournal. In: Proceedings of International Conference on Computational Science and Computational Intelligence, pp. 314–319 (2014)Google Scholar
  10. 10.
    Backstrom, L.; Huttenlocher, D.; Kleinberg, J.: Group formation in large social networks: membership, growth, and evolution. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 44–54 (2006)Google Scholar
  11. 11.
    Wang X., Kwon T., Choi Y., Chen M., Zhang Y.: Characterizing the gaming traffic of world of Warcraft: from game scenarios to network access technologies. IEEE Netw. 26(1), 27–34 (2012)CrossRefGoogle Scholar
  12. 12.
    Patil, A.; Liu, J.; Gao, J.: Predicting group stability in online social networks. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 1021–1030 (2013)Google Scholar
  13. 13.
    Sagar, A.B.: Dependability of social network groups. In: Proceedings of the CUBE International Information Technology Conference, pp. 694–700 (2012)Google Scholar
  14. 14.
    Banerjee A., Dutta P., Ghosh S.: Experience based energy efficient reactive routing protocol (EXERP) for mobile ad-hoc networks. Arab. J. Sci. Eng. 39(2), 891–901 (2014)CrossRefGoogle Scholar
  15. 15.
    Li, C.T.; Shan, M.K.: Composing activity groups in social networks. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp. 2375–2378 (2012)Google Scholar
  16. 16.
    Liang D., Ma S.: An extended Steiner tree optimization problem and its applications. Chin. J. Comput. 19(12), 895–902 (1996)Google Scholar
  17. 17.
    Mislove, A.; Marcon, M.; Gummadi, K.P.: Measurement and analysis of online social networks. In: Proceedings of the 7th ACM SIGCOMM Conference on Internet measurement, pp. 29–42 (2007)Google Scholar
  18. 18.
    Sabater J., Sierra C.: Review on computational trust and reputation models. Artif. Intell. Rev. 24(1), 33–60 (2005)CrossRefMATHGoogle Scholar
  19. 19.
    Unnikrishnan, J.; Naini, F.M.: De-anonymizing private data by matching statistics. In: Proceedings of the 51st Annual Allerton Conference on Communication, Control, and Computing, pp. 1616–1623 (2013)Google Scholar
  20. 20.
    Kannadhasan, S.; KarthiKeyan, G.; Sethupathi, V.: A graph theory based energy efficient clustering techniques in wireless sensor networks. In: Proceedings of the IEEE Conference on Information and Communication Technologies, pp. 151–155 (2013)Google Scholar
  21. 21.
    Saatci, E.; Akan, A.: Generalized Gauss distribution noise model for respiratory parameter estimation. In: Proceedings of the 17th Signal Processing and Communications Applications Conference, pp. 141–144 (2009)Google Scholar

Copyright information

© King Fahd University of Petroleum and Minerals 2015

Authors and Affiliations

  1. 1.Department of Computer ScienceTongji UniversityShanghaiPeople’s Republic of China

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