World Wide Web

, Volume 18, Issue 3, pp 661–679 | Cite as

Ranked content advertising in online social networks

  • Weixiong Rao
  • Lei Chen
  • Ilaria Bartolini


Online social networks (OSNs) such as Twitter, Digg and Facebook have become popular. Users post news, photos and videos, etc. and followers of such users then view and comment the posted information. In general, we call the users who produce the information as the information producers, and the users who view the information as the information consumers. The recently popular targeted information advertising systems enable the producers to target users (i.e., consumers). A key problem of the advertising system is to efficiently find the top-k most desirable targeted users, who next will view the advertised information and perform potential e-commerce activities. Unfortunately, state-of-the-art solutions to find the top-k desirable targeted users in large OSNs incur high space cost and slow running time. In this paper, we focus on designing efficient algorithms to overcome such efficiency issues. Experimental results, over synthetic and real data sets, demonstrate the effectiveness and efficiency of our algorithms.


Top-k query Shortest path distance Efficiency 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)CrossRefGoogle Scholar
  2. 2.
    Ahuja, R.K., Mehlhorn, K., Orlin, J.B., Tarjan, R.E.: Faster algorithms for the shortest path problem. J. ACM 37(2), 213–223 (1990)CrossRefzbMATHMathSciNetGoogle Scholar
  3. 3.
    Amer-Yahia, S., Benedikt, M., Lakshmanan, L.V.S., Stoyanovich, J.: Efficient network aware search in collaborative tagging sites. PVLDB 1(1), 710–721 (2008)Google Scholar
  4. 4.
    Atazky, R., Barone, E.: Advertising and Incentives Over a Social Network, Aug. 30. US Patent App. 11/512,595 (2006)Google Scholar
  5. 5.
    Bartolini, I., Zhang, Z., Papadias, D.: Collaborative filtering with personalized skylines. IEEE Trans. Knowl. Data Eng. 23(2), 190–203 (2011)CrossRefGoogle Scholar
  6. 6.
    Cao, P., Wang, Z.: Efficient top-k query calculation in distributed networks. In: PODC, pp. 206–215 (2004)Google Scholar
  7. 7.
    Chiang, M.-F., Peng, W.-C., Yu, P.S.: Exploring latent browsing graph for question answering recommendation. World Wide Web 15(5–6), 603–630 (2012)CrossRefGoogle Scholar
  8. 8.
    Dijkstra, E.W.: A note on two problems in connexion with graphs. Numer. Math. 1, 269–271 (1959)CrossRefzbMATHMathSciNetGoogle Scholar
  9. 9.
    Eppstein, D., Wang, J.Y.: A steady state model for graph power laws. In: 2nd International Workshop Web Dynamics (2002)Google Scholar
  10. 10.
    Fagin, R., Lotem, A., Naor, M.: Optimal aggregation algorithms for middleware. J. Comput. Syst. Sci. 66(4), 614–656 (2003)CrossRefzbMATHMathSciNetGoogle Scholar
  11. 11.
    Ferman, A.M., Errico, J.H., van Beek, P., Sezan, M.I.: Content-based filtering and personalization using structured metadata. In: JCDL, p. 393 (2002)Google Scholar
  12. 12.
    Goldberg, A.V., Harrelson, C.: Computing the shortest path: a search meets graph theory. In: SODA, pp. 156–165 (2005)Google Scholar
  13. 13.
    Hadija, Z., Barnes, S.B., Hair, N.: Why we ignore social networking advertising. Qual. Mark. Res. Int. J. 15(1), 19–32 (2012)CrossRefGoogle Scholar
  14. 14.
    Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: SIGIR, pp. 230–237 (1999)Google Scholar
  15. 15.
    Holzer, M., Schulz, F., Wagner, D.: Engineering multilevel overlay graphs for shortest-path queries. In: ACM Journal of Experimental Algorithmics, p. 13 (2008)Google Scholar
  16. 16.
    Ilyas, I.F., Beskales, G., Soliman, M.A.: A survey of top- query processing techniques in relational database systems. ACM Comput. Surv. 40(4) (2008)Google Scholar
  17. 17.
    Machanavajjhala, A., Vee, E., Garofalakis, M.N., Shanmugasundaram, J.: Scalable ranked publish/subscribe. PVLDB 1(1), 451–462 (2008)Google Scholar
  18. 18.
    Michel, S., Triantafillou, P., Weikum, G.: Klee: a framework for distributed top-k query algorithms. In: VLDB, pp. 637–648 (2005)Google Scholar
  19. 19.
    O’Madadhain, J., Fisher, D., White, S., Boey, Y.-B.: JUNG: The Java Universal Network/Graph Framework. http://jung.sourceforge.netGoogle Scholar
  20. 20.
    Roy, A., Nilakant, K., Dalibard, V., Yoneki, E.: Mitigating i/o latency in ssd-based graph traversal. In: University of Cambridge, Computer Laboratory, Technical report (UCAM-CL-TR-823) (2012)Google Scholar
  21. 21.
    Sadoghi, M., Jacobsen, H.-A.: Be-tree: an index structure to efficiently match boolean expressions over high-dimensional discrete space. In: SIGMOD Conference, pp. 637–648 (2011)Google Scholar
  22. 22.
    Schenkel, R., Crecelius, T., Kacimi, M., Michel, S., Neumann, T., Parreira, J.X., Weikum, G.: Efficient top-k querying over social-tagging networks. In: SIGIR, pp. 523–530 (2008)Google Scholar
  23. 23.
    Singla, P., Richardson, M.: Yes, there is a correlation: from social networks to personal behavior on the web. In: WWW, pp. 655–664 (2008)Google Scholar
  24. 24.
    Theobald, M., Weikum, G., Schenkel, R.: Top-k query evaluation with probabilistic guarantees. In: VLDB, pp. 648–659 (2004)Google Scholar
  25. 25.
    Thorup, M.: Undirected single-source shortest paths with positive integer weights in linear time. J. ACM 46(3), 362–394 (1999)CrossRefzbMATHMathSciNetGoogle Scholar
  26. 26.
    Ukkonen, A., Castillo, C., Donato, D., Gionis, A.: Searching the Wikipedia with contextual information. In: CIKM, pp. 1351–1352 (2008)Google Scholar
  27. 27.
    Vieira, M.V., Fonseca, B.M., Damazio, R., Golgher, P.B., de Castro Reis, D., Ribeiro-Neto, B.A.: Efficient search ranking in social networks. In: CIKM, pp. 563–572 (2007)Google Scholar
  28. 28.
    Whang, S., Brower, C., Shanmugasundaram, J., Vassilvitskii, S., Vee, E., Yerneni, R., Garcia-Molina, H.: Indexing boolean expressions. PVLDB 2(1), 37–48 (2009)Google Scholar
  29. 29.
    Wu, J., Chen, L., YU, Q., Han, P., Wu, Z.: Trust-aware media recommendation in heterogeneous social networks. In: World Wide Web, pp. 1–19 (2013)Google Scholar
  30. 30.
  31. 31.
    Yang, W.-S., Dia, J.-B.: Discovering cohesive subgroups from social networks for targeted advertising. Expert Syst. Appl. 34(3), 2029–2038 (2008)CrossRefGoogle Scholar
  32. 32.
    Yang, W.-S., Dia, J.-B., Cheng, H.-C., Lin, H.-T.: Mining social networks for targeted advertising. In: HICSS (2006)Google Scholar
  33. 33.
    Yoneki, E., Roy, A.: Scale-up graph processing: a storage-centric view. In: First International Workshop on Graph Data Management Experiences and Systems, GRADES ’13, pp. 8:1–8:6. ACM, New York (2013)Google Scholar
  34. 34.
    Zhu, Y.: Measurement and analysis of an online content voting network: a case study of digg. In: WWW 2010, pp 1039–1048 (2010)Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringThe Hong Kong University of Science and TechnologyKowloonChina
  2. 2.Department of Computer Science and EngineeringUniversity of BolognaBolognaItaly

Personalised recommendations