Skip to main content
SpringerLink
Log in

Ranked content advertising in online social networks

  • Published:
World Wide Web Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  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)

    Article  Google Scholar 

  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)

    Article  MATH  MathSciNet  Google Scholar 

  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. Atazky, R., Barone, E.: Advertising and Incentives Over a Social Network, Aug. 30. US Patent App. 11/512,595 (2006)

  5. Bartolini, I., Zhang, Z., Papadias, D.: Collaborative filtering with personalized skylines. IEEE Trans. Knowl. Data Eng. 23(2), 190–203 (2011)

    Article  Google Scholar 

  6. Cao, P., Wang, Z.: Efficient top-k query calculation in distributed networks. In: PODC, pp. 206–215 (2004)

  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)

    Article  Google Scholar 

  8. Dijkstra, E.W.: A note on two problems in connexion with graphs. Numer. Math. 1, 269–271 (1959)

    Article  MATH  MathSciNet  Google Scholar 

  9. Eppstein, D., Wang, J.Y.: A steady state model for graph power laws. In: 2nd International Workshop Web Dynamics (2002)

  10. Fagin, R., Lotem, A., Naor, M.: Optimal aggregation algorithms for middleware. J. Comput. Syst. Sci. 66(4), 614–656 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  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)

  12. Goldberg, A.V., Harrelson, C.: Computing the shortest path: a search meets graph theory. In: SODA, pp. 156–165 (2005)

  13. Hadija, Z., Barnes, S.B., Hair, N.: Why we ignore social networking advertising. Qual. Mark. Res. Int. J. 15(1), 19–32 (2012)

    Article  Google Scholar 

  14. Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: SIGIR, pp. 230–237 (1999)

  15. Holzer, M., Schulz, F., Wagner, D.: Engineering multilevel overlay graphs for shortest-path queries. In: ACM Journal of Experimental Algorithmics, p. 13 (2008)

  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)

  17. Machanavajjhala, A., Vee, E., Garofalakis, M.N., Shanmugasundaram, J.: Scalable ranked publish/subscribe. PVLDB 1(1), 451–462 (2008)

    Google Scholar 

  18. Michel, S., Triantafillou, P., Weikum, G.: Klee: a framework for distributed top-k query algorithms. In: VLDB, pp. 637–648 (2005)

  19. O’Madadhain, J., Fisher, D., White, S., Boey, Y.-B.: JUNG: The Java Universal Network/Graph Framework. http://jung.sourceforge.net

  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)

  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)

  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)

  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)

  24. Theobald, M., Weikum, G., Schenkel, R.: Top-k query evaluation with probabilistic guarantees. In: VLDB, pp. 648–659 (2004)

  25. Thorup, M.: Undirected single-source shortest paths with positive integer weights in linear time. J. ACM 46(3), 362–394 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  26. Ukkonen, A., Castillo, C., Donato, D., Gionis, A.: Searching the Wikipedia with contextual information. In: CIKM, pp. 1351–1352 (2008)

  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)

  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. 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)

  30. Yahoo! advertising targeting options. http://advertising.yahoo.com/central/marketing/targeting.html

  31. Yang, W.-S., Dia, J.-B.: Discovering cohesive subgroups from social networks for targeted advertising. Expert Syst. Appl. 34(3), 2029–2038 (2008)

    Article  Google Scholar 

  32. Yang, W.-S., Dia, J.-B., Cheng, H.-C., Lin, H.-T.: Mining social networks for targeted advertising. In: HICSS (2006)

  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)

  34. Zhu, Y.: Measurement and analysis of an online content voting network: a case study of digg. In: WWW 2010, pp 1039–1048 (2010)

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China

    Weixiong Rao & Lei Chen

  2. Department of Computer Science and Engineering, University of Bologna, Bologna, Italy

    Ilaria Bartolini

Authors
  1. Weixiong Rao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ilaria Bartolini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Weixiong Rao.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rao, W., Chen, L. & Bartolini, I. Ranked content advertising in online social networks. World Wide Web 18, 661–679 (2015). https://doi.org/10.1007/s11280-014-0276-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11280-014-0276-2

Keywords

Search

Navigation